EDBT 2026 Demo / reviewers in the wild / expert
Krishnan Srinivasan
dblp:02/4773
· DBLP profile ↗
21ranked-venue papers
9as first author
3since 2021 · last 2025
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 18 · 9 first-author · 2 since 2021Artificial intelligence and machine learning · 8 · 1 first-author · 3 since 2021Applied, interdisciplinary, general and emerging computing · 2 · 1 first-authorSoftware engineering, systems software and programming languages · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
7 papers |
Motion planning and robot control · 40% Robot manipulation · 34% Reinforcement learning · 21% | |
| Human-computer interaction and pervasive computing
1 paper |
Human-robot interaction · 87% Accessibility and assistive technology · 13% |
Topics — the 21 heaviest of 23, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Motion planning and robot control
robot learning |
2.0 | 4 | 2024 | Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration · ICRA 2024 Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks · IEEE Trans. Robotics 2020 Controlling Assistive Robots with Learned Latent Actions · ICRA 2020 |
Robotics › Robot manipulation
contact-rich manipulation |
0.8 | 2 | 2020 | Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks · IEEE Trans. Robotics 2020 Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks · ICRA 2019 |
Robotics › Robot manipulation › dexterous manipulation
in-hand manipulation |
0.8 | 2 | 2020 | Learning Hierarchical Control for Robust In-Hand Manipulation · ICRA 2020 Learning Modes of Within-Hand Manipulation · ICRA 2018 |
Robotics › Motion planning and robot control › robot learning › robot policy learning
generalist robot policy |
0.8 | 1 | 2024 | Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration · ICRA 2024 |
Machine learning › Reinforcement learning
model-based reinforcement learning |
0.8 | 1 | 2024 | Adaptive Horizon Actor-Critic for Policy Learning in Contact-Rich Differentiable Simulation · ICML 2024 |
Robotics › Robot manipulation
dexterous manipulation |
0.4 | 1 | 2020 | Learning Hierarchical Control for Robust In-Hand Manipulation · ICRA 2020 |
Robotics › Motion planning and robot control › robot control
hierarchical control |
0.4 | 1 | 2020 | Learning Hierarchical Control for Robust In-Hand Manipulation · ICRA 2020 |
Machine learning › Reinforcement learning › function approximation › representation learning for reinforcement learning
latent action learning |
0.4 | 1 | 2020 | Controlling Assistive Robots with Learned Latent Actions · ICRA 2020 |
Robotics › Robot manipulation
manipulation primitives |
0.4 | 1 | 2020 | Learning Hierarchical Control for Robust In-Hand Manipulation · ICRA 2020 |
Machine learning › Reinforcement learning › sample efficiency
sample-efficient policy learning |
0.4 | 1 | 2020 | Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks · IEEE Trans. Robotics 2020 |
Human-robot interaction
assistive robotics |
0.4 | 1 | 2020 | Controlling Assistive Robots with Learned Latent Actions · ICRA 2020 |
Human-robot interaction
teleoperation |
0.4 | 1 | 2020 | Controlling Assistive Robots with Learned Latent Actions · ICRA 2020 |
Machine learning › Representation and self-supervised learning
multimodal representation learning |
0.4 | 1 | 2019 | Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks · ICRA 2019 |
Robotics › Motion planning and robot control
robot control |
0.2 | 1 | 2024 | Adaptive Horizon Actor-Critic for Policy Learning in Contact-Rich Differentiable Simulation · ICML 2024 |
Machine learning › Reinforcement learning
policy learning |
0.1 | 1 | 2020 | Learning Hierarchical Control for Robust In-Hand Manipulation · ICRA 2020 |
Robotics › Robot manipulation › assembly
peg insertion |
0.1 | 1 | 2019 | Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks · ICRA 2019 |
Robotics › Robot manipulation
tactile sensing |
0.1 | 1 | 2018 | Learning Modes of Within-Hand Manipulation · ICRA 2018 |
Interconnection networks and networks-on-chip › network-on-chip design
noc synthesis |
0.1 | 1 | 2008 | Automated Techniques for Synthesis of Application-Specific Network-on-Chip Architectures · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2008 |
Electronic design automation › analog circuit synthesis
topology synthesis |
0.1 | 1 | 2008 | Automated Techniques for Synthesis of Application-Specific Network-on-Chip Architectures · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2008 |
Electronic design automation
design space exploration |
0.0 | 1 | 2008 | Automated Techniques for Synthesis of Application-Specific Network-on-Chip Architectures · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2008 |
Electronic design automation
high-level synthesis |
0.0 | 1 | 2008 | Automated Techniques for Synthesis of Application-Specific Network-on-Chip Architectures · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2008 |
Methods — techniques the papers use, named apart from their topics
deep reinforcement learning · 1.2shared autonomy · 0.9self-supervised learning · 0.8transformer policy · 0.8large-scale pretraining · 0.8first-order model-based reinforcement learning · 0.8actor-critic · 0.8model-based control · 0.4latent variable models · 0.4latent variable model · 0.4extra trees · 0.3core-to-router mapping · 0.1approximation algorithm · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | ACGD: Visual Multitask Policy Learning with Asymmetric Critic Guided DistillationabstractWe present Asymmetric Critic Guided Distillation, ACGD, a framework for learning multi-task dexterous manipulation policies that can manipulate articulated objects using images as input. ACGD is a scalable student-teacher distillation approach that utilizes behavior cloning to distill multiple expert policies into a single vision-based, multi-task student policy for dexterous manipulation. The expert policies are trained with traditional RL techniques with access to privileged state information of both the robot and the manipulated object, while the distilled student policy operates under realistic sensory constraints, specifically using only camera images and robot proprioception. During distillation, we use an expert-critic that provides action labels and value estimates to refine the student’s action sampling through a dual IL/RL objective. In the multi-task setting, we achieve this through an aggregate critic for different single-task experts. Our approach exhibits strong performance compared to a number of state-of-the-art imitation learning (IL) and reinforcement learning (RL) baselines. We evaluate across a variety of multi-task dexterous manipulation benchmarks including bimanual manipulation, single-hand object articulation tasks, and a tendon-actuated hand and achieves state-of-the-art performance with 10-15% improvement over the baseline algorithms. Visit our website for more details. Krishnan Srinivasan, Jie Xu 0028, Henry Ang, Eric Heiden, Dieter Fox, Jeannette Bohg, Animesh Garg |
IROS | 1 |
| 2024 | Adaptive Horizon Actor-Critic for Policy Learning in Contact-Rich Differentiable SimulationabstractModel-Free Reinforcement Learning (MFRL), leveraging the policy gradient theorem, has demonstrated considerable success in continuous control tasks. However, these approaches are plagued by high gradient variance due to zeroth-order gradient estimation, resulting in suboptimal policies. Conversely, First-Order Model-Based Reinforcement Learning (FO-MBRL) methods employing differentiable simulation provide gradients with reduced variance but are susceptible to sampling error in scenarios involving stiff dynamics, such as physical contact. This paper investigates the source of this error and introduces Adaptive Horizon Actor-Critic (AHAC), an FO-MBRL algorithm that reduces gradient error by adapting the model-based horizon to avoid stiff dynamics. Empirical findings reveal that AHAC outperforms MFRL baselines, attaining 40% more reward across a set of locomotion tasks and efficiently scaling to high-dimensional control environments with improved wall-clock-time efficiency. adaptive-horizon-actor-critic.github.io Ignat Georgiev, Krishnan Srinivasan, Jie Xu 0028, Eric Heiden, Animesh Garg |
ICML | 2 |
| 2024 | Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment CollaborationabstractLarge, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io. Abigail O'Neill, Abhiram Maddukuri, Abhishek Gupta 0004, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, Albert Tung, Alex Bewley, Alex Irpan, Alexander Khazatsky, Anant Rai, Anchit Gupta, Andrew E. Wang, Anikait Singh, Animesh Garg, Aniruddha Kembhavi, Annie Xie, Anthony Brohan, Antonin Raffin, Archit Sharma, Arefeh Yavary, Arhan Jain, Ashwin Balakrishna, Ayzaan Wahid, Ben Burgess-Limerick, Bernhard Schölkopf, Blake Wulfe, Brian Ichter, Cewu Lu, Charles Xu 0003, Charlotte Le, Chelsea Finn, Chen Wang 0053, Chenfeng Xu, Cheng Chi 0001, Chenguang Huang, Christine Chan, Christopher Agia, Chuer Pan, Chuyuan Fu, Coline Devin, Danfei Xu, Daniel Morton, Danny Drieß, Daphne Chen, Deepak Pathak, Dhruv Shah, Dieter Büchler, Dinesh Jayaraman, Dmitry Kalashnikov, Dorsa Sadigh, Edward Johns, Ethan Paul Foster, Fangchen Liu, Federico Ceola, Fei Xia 0002, Feiyu Zhao, Freek Stulp, Gaoyue Zhou, Gaurav S. Sukhatme, Gautam Salhotra, Gilbert Feng, Giulio Schiavi, Glen Berseth, Gregory Kahn, Guanzhi Wang, Hao Su 0001, Haoshu Fang, Henghui Bao, Heni Ben Amor, Henrik I. Christensen, Hiroki Furuta, Homer Walke, Hongjie Fang, Huy Ha, Igor Mordatch, Ilija Radosavovic, Isabel Leal, Jacky Liang, Jad Abou-Chakra, Jaehyung Kim 0001, Jaimyn Drake, Jan Peters 0001, Jan Schneider 0007, Jasmine Hsu, Jeannette Bohg, Jeffrey T. Bingham, Jensen Gao, Jiaheng Hu, Jiajun Wu 0001, Jiankai Sun, Jianlan Luo, Jiayuan Gu, Jie Tan 0001, Jihoon Oh, Jimmy Wu, Jingpei Lu, Jitendra Malik, João Silvério, Joey Hejna, Jonathan Booher, Jonathan Tompson, Jonathan Yang, Jordi Salvador, Joseph J. Lim, Junhyek Han, Kanishka Rao, Karl Pertsch, Karol Hausman, Keegan Go, Keerthana Gopalakrishnan, Kenneth Y. Goldberg, Kendra Byrne, Kenneth Oslund, Kento Kawaharazuka, Kevin Black, Kevin Zhang 0002, Kiana Ehsani, Kiran Lekkala, Kirsty Ellis, Krishan Rana, Krishnan Srinivasan, Kuan Fang, Kunal Pratap Singh, Kuo-Hao Zeng, Kyle Hatch, Kyle Hsu, Laurent Itti, Yunliang Chen 0001, Lerrel Pinto, Li Fei-Fei 0001, Liam Tan, Linxi Fan, Lionel Ott, Lisa Lee, Luca Weihs, Magnum Chen, Marion Lepert, Marius Memmel, Masayoshi Tomizuka, Masha Itkina, Mateo Guaman Castro, Max Spero, Maximilian Du, Michael Ahn, Michael C. Yip, Mingtong Zhang 0003, Mingyu Ding, Minho Heo, Mohan Kumar Srirama, Mohit Sharma 0001, Moo Jin Kim, Naoaki Kanazawa, Nicklas Hansen 0001, Nicolas Heess, Nikhil J. Joshi, Niko Sünderhauf, Norman Di Palo, Nur Muhammad Shafiullah, Oier Mees, Oliver Kroemer, Osbert Bastani, Pannag R. Sanketi, Patrick Tree Miller, Patrick Yin, Paul Wohlhart, Peng Xu 0010, Peter David Fagan, Peter Mitrano, Pierre Sermanet, Pieter Abbeel, Priya Sundaresan, Qiuyu Chen, Rafael Rafailov, Ria Doshi, Roberto Martin Martin, Rohan Baijal, Rosario Scalise, Rose Hendrix, Roy Lin, Runjia Qian, Russell Mendonca, Rutav Shah, Ryan Hoque, Ryan Julian, Samuel Bustamante-Gomez, Sean Kirmani, Sergey Levine, Sherry Moore, Shikhar Bahl, Shivin Dass, Shubham D. Sonawani, Shuran Song, Sichun Xu, Siddhant Haldar, Siddharth Karamcheti, Simeon Adebola, Simon Guist, Soroush Nasiriany, Stefan Schaal, Stefan Welker, Stephen Tian, Subramanian Ramamoorthy, Sudeep Dasari, Suneel Belkhale, Sungjae Park, Suraj Nair 0003, Suvir Mirchandani, Takayuki Osa, Tanmay Gupta, Tatsuya Harada, Tatsuya Matsushima, Ted Xiao, Thomas Kollar, Tianhe Yu, Tianli Ding, Todor Davchev, Tony Z. Zhao, Travis Armstrong, Trevor Darrell, Trinity Chung, Vidhi Jain, Vincent Vanhoucke, Wolfram Burgard, Xiaolong Wang 0004, Xinghao Zhu, Xinyang Geng, Liangwei Xu, Yecheng Jason Ma 0001, Yejin Kim 0003, Yevgen Chebotar, Yilin Wu 0003, Yonatan Bisk, Yoonyoung Cho, Youngwoon Lee, Yuchen Cui, Yueh-Hua Wu, Yujin Tang, Yuke Zhu, Yunchu Zhang, Yunfan Jiang 0001, Yunshuang Li, Yunzhu Li, Yusuke Iwasawa, Yutaka Matsuo, Zehan Ma, Zichen Jeff Cui, Zichen Zhang 0016, Zipeng Lin |
ICRA | 135 |
| 2020 | Learning Hierarchical Control for Robust In-Hand ManipulationabstractRobotic in-hand manipulation has been a longstanding challenge due to the complexity of modelling hand and object in contact and of coordinating finger motion for complex manipulation sequences. To address these challenges, the majority of prior work has either focused on model-based, low-level controllers or on model-free deep reinforcement learning that each have their own limitations. We propose a hierarchical method that relies on traditional, model-based controllers on the low-level and learned policies on the mid-level. The low-level controllers can robustly execute different manipulation primitives (reposing, sliding, flipping). The mid-level policy orchestrates these primitives. We extensively evaluate our approach in simulation with a 3-fingered hand that controls three degrees of freedom of elongated objects. We show that our approach can move objects between almost all the possible poses in the workspace while keeping them firmly grasped. We also show that our approach is robust to inaccuracies in the object models and to observation noise. Finally, we show how our approach generalizes to objects of other shapes. Tingguang Li, Krishnan Srinivasan, Max Q.-H. Meng, Wenzhen Yuan 0001, Jeannette Bohg |
ICRA | 2 |
| 2020 | Controlling Assistive Robots with Learned Latent ActionsabstractAssistive robotic arms enable users with physical disabilities to perform everyday tasks without relying on a caregiver. Unfortunately, the very dexterity that makes these arms useful also makes them challenging to teleoperate: the robot has more degrees-of-freedom than the human can directly coordinate with a handheld joystick. Our insight is that we can make assistive robots easier for humans to control by leveraging latent actions. Latent actions provide a lowdimensional embedding of high-dimensional robot behavior: for example, one latent dimension might guide the assistive arm along a pouring motion. In this paper, we design a teleoperation algorithm for assistive robots that learns latent actions from task demonstrations. We formulate the controllability, consistency, and scaling properties that user-friendly latent actions should have, and evaluate how different lowdimensional embeddings capture these properties. Finally, we conduct two user studies on a robotic arm to compare our latent action approach to both state-of-the-art shared autonomy baselines and a teleoperation strategy currently used by assistive arms. Participants completed assistive eating and cooking tasks more efficiently when leveraging our latent actions, and also subjectively reported that latent actions made the task easier to perform. The video accompanying this paper can be found at: https://youtu.be/wjnhrzugBj4. Dylan P. Losey, Krishnan Srinivasan, Ajay Mandlekar, Animesh Garg, Dorsa Sadigh |
ICRA | 2 |
| 2020 | Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich TasksabstractContact-rich manipulation tasks in unstructured environments often require both haptic and visual feedback. It is nontrivial to manually design a robot controller that combines these modalities, which have very different characteristics. While deep reinforcement learning has shown success in learning control policies for high-dimensional inputs, these algorithms are generally intractable to train directly on real robots due to sample complexity. In this article, we use self-supervision to learn a compact and multimodal representation of our sensory inputs, which can then be used to improve the sample efficiency of our policy learning. Evaluating our method on a peg insertion task, we show that it generalizes over varying geometries, configurations, and clearances, while being robust to external perturbations. We also systematically study different self-supervised learning objectives and representation learning architectures. Results are presented in simulation and on a physical robot. Michelle A. Lee, Yuke Zhu, Peter Zachares, Matthew Tan, Krishnan Srinivasan, Silvio Savarese, Li Fei-Fei 0001, Animesh Garg, Jeannette Bohg |
IEEE Trans. Robotics | 5 |
| 2019 | Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich TasksabstractContact-rich manipulation tasks in unstructured environments often require both haptic and visual feedback. However, it is non-trivial to manually design a robot controller that combines modalities with very different characteristics. While deep reinforcement learning has shown success in learning control policies for high-dimensional inputs, these algorithms are generally intractable to deploy on real robots due to sample complexity. We use self-supervision to learn a compact and multimodal representation of our sensory inputs, which can then be used to improve the sample efficiency of our policy learning. We evaluate our method on a peg insertion task, generalizing over different geometry, configurations, and clearances, while being robust to external perturbations. We present results in simulation and on a real robot. Michelle A. Lee, Yuke Zhu, Krishnan Srinivasan, Parth Shah 0003, Silvio Savarese, Li Fei-Fei 0001, Animesh Garg, Jeannette Bohg |
ICRA | 3 |
| 2018 | Learning Modes of Within-Hand ManipulationabstractIn this work, we investigate methods to detect four phenomena (modes) that occur during prehensile fingertip-based within-hand manipulation without the use of tactile sensors. By using actuator states and visual data, we aim to recognize different modes of operation such as interpreting if the hand is about to drop the object, if the object will begin to slide on the fingers, or if the system is at or near a singularity. For this purpose, we utilize supervised learning techniques, which allow us to detect the modes without the use of a mechanical model of the system. We analyze the individual roles of specific features available through both the actuator and visual data, and identify the ones that have the most significance for detecting the operation modes. Our results show classification performance of 96% (using either Extra Trees, Gradient Boosting, or SVM) when using combined actuator and visual features. Interestingly, we were able to achieve a 94%classification rate using only actuator information, and 93 % using only visual information. Overall, the classifiers identified actuator positions, actuator loads, and commanded velocities as the most important features for detecting a mode. These results have implications for enabling the control of within-hand manipulation movements utilizing a minimal amount of sensory information without a model of the hand/object system. Berk Çalli, Krishnan Srinivasan, Andrew S. Morgan, Aaron M. Dollar |
ICRA | 2 |
| 2017 | Graph-based Neural Multi-Document SummarizationabstractWe propose a neural multi-document summarization (MDS) system that incorporates sentence relation graphs.We employ a Graph Convolutional Network (GCN) on the relation graphs, with sentence embeddings obtained from Recurrent Neural Networks as input node features.Through multiple layer-wise propagation, the GCN generates high-level hidden sentence features for salience estimation.We then use a greedy heuristic to extract salient sentences while avoiding redundancy.In our experiments on DUC 2004, we consider three types of sentence relation graphs and demonstrate the advantage of combining sentence relations in graphs with the representation power of deep neural networks.Our model improves upon traditional graph-based extractive approaches and the vanilla GRU sequence model with no graph, and it achieves competitive results against other state-of-the-art multidocument summarization systems. Michihiro Yasunaga, Rui Zhang 0037, Kshitijh Meelu, Ayush Pareek, Krishnan Srinivasan, Dragomir R. Radev |
CoNLL | 5 |
| 2011 | Sampling-based garbage collection metadata management scheme for flash-based storageabstractExisting garbage collection algorithms for the flash-based storage use score-based heuristics to select victim blocks for reclaiming free space and wear leveling. The score for a block is estimated using metadata information such as age, block utilization, and erase count. To quickly find a victim block, these algorithms maintain a priority queue in the SRAM of the storage controller. This priority queue takes O(K) space, where K stands for flash storage capacity in total number of blocks. As the flash capacity scales to larger size, K also scales to larger value. However, due to higher price per byte, SRAM will not scale proportionately. In this case, due to SRAM scarcity, it will be challenging to implement a larger priority queue in the limited SRAM of a large-capacity flash storage. In addition to space issue, with any update in the metadata information, the priority queue needs to be continuously updated, which takes O(lg(K)) operations. This computation overhead also increases with the increase of flash capacity. In this paper, we have taken a novel approach to solve the garbage collection metadata management problem of a large-capacity flash storage. We propose a sampling-based approach to approximate existing garbage collection algorithms in the limited SRAM space. Since these algorithms are heuristic-based, our sampling-based algorithm will perform as good as unsampled (original) algorithm, if we choose good samples to make garbage collection decisions. We propose a very simple policy to choose samples. Our experimental results show that small number of samples are good enough to emulate existing garbage collection algorithms. Biplob K. Debnath, Krishnan Srinivasan, Weijun Xiao, David J. Lilja, David Hung-Chang Du |
MSST | 2 |
| 2009 | Design of Network-on-Chip Architectures With a Genetic Algorithm-Based TechniqueabstractThe network-on-chip (NoC) design problem requires the generation of a power and resource efficient interconnection architecture that can support the communication requirements for the SoC with the desired performance. This paper presents a genetic algorithm-based automated design technique that synthesizes an application specific NoC topology and routes the communication traces on the interconnection network. The technique operates on the system-level floorplan of the system on chip (SoC) and accounts for the power consumption in the physical links and the routers. The design technique solves a multi-objective problem of minimizing the power consumption and the router resources. It generates a Pareto curve of the solution set, such that each point in the curve represents a tradeoff between power consumption and associated number of NoC routers. The performance and quality of solutions produced by the technique are evaluated by experimentation with benchmark applications and comparisons with existing approaches. Glenn Leary, Krishnan Srinivasan, Krishna Mehta, Karam S. Chatha |
IEEE Trans. Very Large Scale Integr. Syst. | 2 |
| 2008 | Automated Techniques for Synthesis of Application-Specific Network-on-Chip ArchitecturesabstractThis paper addresses the automated synthesis of a custom network-on-chip architecture whose topology is optimized for the specific communication requirements of the target device. The optimization objectives include power consumption and resource usage. This paper presents a two-stage synthesis approach consisting of the following: (1) core to router mapping and (2) custom topology and route generation. In particular, it presents an optimal technique for core to router mapping [stage (1)] and a factor-2 approximation algorithm for custom topology generation [stage (2)]. The superior quality of the techniques is established by experimentation with benchmark applications and by comparisons with existing approaches. Karam S. Chatha, Krishnan Srinivasan, Goran Konjevod |
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. | 2 |
| 2007 | Application Specific Network-on-Chip Design with Guaranteed Quality Approximation AlgorithmsabstractNetwork-on-chip (NoC) architectures with optimized topologies have been shown to be superior to regular architectures (such as mesh) for application specific multiprocessor system-on-chip (MPSoC) devices. The application specific NoC design problem takes as input the system-level floorplan of the computation architecture, characterized library of NoC components, and the communication performance requirements. The objective is to generate an optimized NoC topology, and routes for the communication traces on the architecture such that the performance requirements are satisfied and power consumption is minimized. The paper discusses a two stage automated approach consisting of i) core to router mapping, and ii) topology and route generation for design of custom NoC architectures. In particular it presents an optimal technique for core to router mapping (stage i), and a factor 2 approximation algorithm for custom topology generation (stage ii). The superior quality of the techniques is established by experimentation with benchmark applications, and comparisons with integer linear programming (ILP) formulations, and heuristic techniques. Krishnan Srinivasan, Karam S. Chatha, Goran Konjevod |
ASP-DAC | 1 |
| 2007 | Integer linear programming and heuristic techniques for system-level low power scheduling on multiprocessor architectures under throughput constraints
Krishnan Srinivasan, Karam S. Chatha |
Integr. | 1 |
| 2007 | ILP and heuristic techniques for system-level design on network processor architecturesabstractNetwork processors incorporate several architectural features, including symmetric multiprocessing (SMP), block multithreading, and multiple memory elements, to support the high-performance requirements of current day applications. This article presents automated system-level design techniques for application development on such architectures. We propose integer linear programming formulations and heuristic techniques for process allocation and data mapping on SMP and block-multithreading-based network processors. The techniques incorporate process transformations and multithreading-aware data mapping to maximize the throughput of the application. The article presents experimental results that evaluate the techniques by implementing network processing applications on the Intel IXP 2400 architecture. Christopher Ostler, Karam S. Chatha, Vijay Ramamurthi, Krishnan Srinivasan |
ACM Trans. Design Autom. Electr. Syst. | 4 |
| 2006 | A low complexity heuristic for design of custom network-on-chip architecturesabstractNetwork-on-chip (NoC) has been proposed to replace traditional bus based architectures to address the global communication challenges in nanoscale technologies. In future SoC architectures, minimizing power consumption continue to be an important design goal. In this paper, we present a novel heuristic technique consisting of system-level physical design, and interconnection network generation that generates custom low power NoC architectures for application specific SoC. We demonstrate the quality of the solutions produced by our technique by experimentation with many benchmarks. Our technique has a low computational complexity, and consumes only 1.25 times the power consumption, and 0.85 times the number of router resources compared to an optimal MILP based technique whose computational complexity is not bounded Krishnan Srinivasan, Karam S. Chatha |
DATE | 1 |
| 2006 | Linear-programming-based techniques for synthesis of network-on-chip architecturesabstractApplication-specific system-on-chip (SoC) design offers the opportunity for incorporating custom network-on-chip (NoC) architectures that are more suitable for a particular application, and do not necessarily conform to regular topologies. This paper presents novel mixed integer linear programming (MILP) formulations for synthesis of custom NoC architectures. The optimization objective of the techniques is to minimize the power consumption subject to the performance constraints. We present a two-stage approach for solving the custom NoC synthesis problem. The power consumption of the NoC architecture is determined by both the physical links and routers. The power consumption of a physical link is dependent upon the length of the link, which in turn, is governed by the layout of the SoC. Therefore, in the first stage, we address the floorplanning problem that determines the locations of the various cores and the routers. In the second stage, we utilize the floorplan from the first stage to generate topology of the NoC and the routes for the various traffic traces. We also present a clustering-based heuristic technique for the second stage to reduce the run times of the MILP formulation. We analyze the quality of the results and solution times of the proposed techniques by extensive experimentation with realistic benchmarks and comparisons with regular mesh-based NoC architectures. Krishnan Srinivasan, Karam S. Chatha, Goran Konjevod |
IEEE Trans. Very Large Scale Integr. Syst. | 1 |
| 2005 | SAGA: synthesis technique for guaranteed throughput NoC architecturesabstractWe present SAGA, a novel genetic algorithm (GA) based technique for synthesis of custom NoC architectures that support guaranteed throughput traffic. The technique accepts as input a communication trace graph, amount of data, period, and deadline for each trace, interconnection network architecture elements, and generates a custom NoC topology, and routing and schedule of the communication traces on the architecture. SAGA minimizes both the energy consumption and area of the design by solving a multi-objective optimization problem. We present a detailed analysis of the quality of the results and the solution times of the proposed technique by extensive experimentation with realistic benchmarks and comparisons with optimal MILP solutions. SAGA is able to generate solutions that are as good as the optimal solutions produced by the MILP formulation. Whereas the MILP formulation run time rises exponentially for even moderately sized graphs, SAGA generates solutions for large graphs in reasonable time. Krishnan Srinivasan, Karam S. Chatha |
ASP-DAC | 1 |
| 2005 | An automated technique for topology and route generation of application specific on-chip interconnection networksabstractNetwork-on-chip (NoC) has been proposed as a solution to the communication challenges of system-on-chip (SoC) design in nanoscale technologies. Application specific SoC design offers the opportunity for incorporating custom NoC architectures that are more suitable for a particular application, and do not necessarily conform to regular topologies. Custom NoC design in nanoscale technologies must address performance requirements, power consumption and physical layout considerations. This paper presents a novel three phase technique that i) generates a performance aware layout of the SoC, ii) maps the cores of the SoC to routers, and iii) generates a unique route for every trace that satisfies the performance and architectural constraints. We present an analysis of the quality of the results of the proposed technique by experimentation with realistic benchmarks. Krishnan Srinivasan, Karam S. Chatha, Goran Konjevod |
ICCAD | 1 |
| 2005 | A technique for low energy mapping and routing in network-on-chip architecturesabstractNetwork-on-chip (NoC) has been proposed as a solution for the global communication challenges of System-on-chip (SoC) design in the nanoscale technologies. NoC design with mesh based topologies requires mapping of cores to router ports, and routing of traffic traces such that the bandwidth and latency constraints are satisfied. We present a novel automated design technique that solves the mesh based NoC design problem with an objective of minimizing the communication energy. In contrast to existing research that only take bandwidth constraints as inputs, our technique solves the NoC design problem in the presence of bandwidth as well as latency constraints. We compare our technique with a recent work called NMAP and an optimal MILP based formulation. We prove that the complexity of our technique is lower than that of NMAP. For the latency constrained case, while NMAP fails on most test cases, our technique is able to generate high quality results. In comparison to the MILP formulation, the results produced by our technique are within 14% of the optimal Krishnan Srinivasan, Karam S. Chatha |
ISLPED | 1 |
| 2004 | Linear Programming based Techniques for Synthesis of Network-on-Chip ArchitecturesabstractNetwork-on-chip (NoC) has been proposed as a solution for the communication challenges of system-on-chip (SoC) design in the nanoscale regime. SoC design offers the opportunity for incorporating custom NoC architectures that are more suitable for a particular application, and do not necessarily conform to regular topologies. This paper presents novel linear programming based techniques for synthesis of custom NoC architectures. In the nanoscale regime, low power consumption would continue to be an important design goal. We first discuss an optimal mixed integer linear programming (MILP) formulation that synthesizes a low power NoC architecture subject to the performance constraints. The MILP formulation is limited by large run times. We next present heuristic techniques that exploit clustering, and 0-1 constraint relaxation to reduce the run times of the formulation. The techniques minimize power as the primary goal, and minimize the number of routers (area) as a secondary goal. We present an analysis of the quality of the results and the solution times of the proposed techniques by extensive experimentation with the realistic benchmarks. The clustering based heuristic generates results whose power consumption is within 11% of the MILP solutions and its average run time is 171.1 seconds. The average run time of the relaxation and rounding based techniques is less than 2 seconds, and the power consumption of their solutions is within 58% of the MILP result. Krishnan Srinivasan, Karam S. Chatha, Goran Konjevod |
ICCD | 1 |