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Mateja Putic

dblp:76/5168 · DBLP profile ↗
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6ranked-venue papers
2as first author
0since 2021 · last 2018
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 5 · 2 first-authorApplied, interdisciplinary, general and emerging computing · 1

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.

Computer architecture, parallel and distributed computing, and storage systems
3 papers
Hardware accelerators and domain-specific architectures · 40% Reconfigurable computing and FPGAs · 26% Energy-efficient computing · 16%
Artificial intelligence
1 paper
Deep learning architectures and training · 100%

Topics — the 9 heaviest of 10, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Reconfigurable computing and FPGAs
dynamic reconfiguration
0.312018
Dyhard-DNN: even more DNN acceleration with dynamic hardware reconfiguration · DAC 2018
Hardware accelerators and domain-specific architectures › machine learning accelerator
neural network accelerator
0.312018
Dyhard-DNN: even more DNN acceleration with dynamic hardware reconfiguration · DAC 2018
Hardware accelerators and domain-specific architectures
systolic array
0.312018
Dyhard-DNN: even more DNN acceleration with dynamic hardware reconfiguration · DAC 2018
GPUs and heterogeneous computing
GPU computing
0.212014
Low Power GPGPU Computation with Imprecise Hardware · DAC 2014
Energy-efficient computing › voltage scaling
dynamic voltage scaling
0.112010
Flexible Circuits and Architectures for Ultralow Power · Proc. IEEE 2010
Integrated circuit design › low-power circuit design
subthreshold circuit design
0.112010
Flexible Circuits and Architectures for Ultralow Power · Proc. IEEE 2010
Energy-efficient computing › low-power design
ultra-low power design
0.112010
Flexible Circuits and Architectures for Ultralow Power · Proc. IEEE 2010
Machine learning › Deep learning architectures and training › neural network inference
DNN inference
0.112018
Dyhard-DNN: even more DNN acceleration with dynamic hardware reconfiguration · DAC 2018
Energy-efficient computing
power management
0.112014
Low Power GPGPU Computation with Imprecise Hardware · DAC 2014

Methods — techniques the papers use, named apart from their topics

dynamic hardware reconfiguration · 0.7SIMD · 0.7imprecise floating point arithmetic · 0.2GPUWattch · 0.2GPGPU-Sim · 0.2
YearPublicationVenuePosition
2018 Dyhard-DNN: even more DNN acceleration with dynamic hardware reconfiguration
abstract
Deep Neural Networks (DNNs) have demonstrated their utility across a wide range of input data types, usable across diverse computing substrates, from edge devices to datacenters. This broad utility has resulted in myriad hardware accelerator architectures. However, DNNs exhibit significant heterogeneity in their computational characteristics, e.g., feature and kernel dimensions, and dramatic variances in computational intensity, even between adjacent layers in one DNN. Consequently, accelerators with static hardware parameters run sub-optimally and leave energy-efficiency margins unclaimed. We propose DyHard-DNNs, where accelerator microarchitectural parameters are dynamically reconfigured during DNN execution to significantly improve metrics of interest. We demonstrate the effectiveness of this approach on a configurable SIMD 2D systolic array and show a 15--65% performance improvement (at iso-power) and 25--90% energy improvement (at iso-latency) over the best static configuration in six mainstream DNN workloads.
Mateja Putic, Swagath Venkataramani, Schuyler Eldridge, Alper Buyuktosunoglu, Pradip Bose, Mircea R. Stan
DAC1
2014 Low Power GPGPU Computation with Imprecise Hardware
abstract
Massively parallel computation in GPUs significantly boosts performance of compute-intensive applications but creates power and thermal issues that limit further performance scaling. This paper demonstrates significant GPGPU power savings by relaxing application accuracy requirements and enabling the use of low power imprecise hardware (IHW). A synthesized set of novel imprecise floating point arithmetic units is presented. GPGPU-Sim and GPUWattch are used to estimate impacts of IHW units on output quality and system-level power consumption, providing a quality-power tradeoff model for application-specific optimization. Experimental results for a 45 nm process show up to 32% power savings with negligible impacts on output quality.
Hang Zhang 0031, Mateja Putic, John C. Lach
DAC2
2013 Balancing Adder for error tolerant applications
abstract
Recent imprecise hardware (IHW) design methodologies present opportunities for achieving gains in nonfunctional efficiency design metrics by allowing errors in computation within error tolerant application domains. This work presents a novel imprecise Error Tolerant Balancing Adder (ETBA) design - an augmentation of the ETAIIM IHW adder that reduces errors by introducing a balance block that detects and corrects carry chain inconsistencies in the ETAIIM but operates off the critical path. Furthermore, this work identifies a common class of killer inputs with high error rates for IHW adders, and demonstrates the ETBA's resilience to these errors. A JPEG decompression case study reveals a 24% reduction in ETBA addition energy-delay product compared to a Kogge-Stone adder with only a 0.2% reduction in SSIM image quality.
Matthew Weber, Mateja Putic, Hang Zhang 0031, John C. Lach, Jiawei Huang 0007
ISCAS2
2010 Flexible Circuits and Architectures for Ultralow Power
abstract
Subthreshold digital circuits minimize energy per operation and are thus ideal for ultralow-power (ULP) applications with low performance requirements. However, a large range of ULP applications continue to face performance constraints at certain times that exceed the capabilities of subthreshold operation. In this paper, we give two different examples to show that designing flexibility into ULP systems across the architecture and circuit levels can meet both the ULP requirements and the performance demands. Specifically, we first present a method that expands on ultradynamic voltage scaling (UDVS) to combine multiple supply voltages with component level power switches to provide more efficient operation at any energy-delay point and low overhead switching between points. This system supports operation across the space from maximum performance, when necessary, to minimum energy, when possible. It thus combines the benefits of single-VDD, multi-VDD, and dynamic voltage scaling (DVS) while improving on them all. Second, we propose that reconfigurable subthreshold circuits can increase applicability for ULP embedded systems. Since ULP devices conventionally require custom circuit design but the manufacturing volume for many ULP applications is low, a subthreshold field programmable gate array (FPGA) offers a cost-effective custom solution with hardware flexibility that makes it applicable across a wide range of applications. We describe the design of a subthreshold FPGA to support ULP operation and identify key challenges to this effort.
Benton H. Calhoun, Joseph F. Ryan 0002, Sudhanshu Khanna, Mateja Putic, John C. Lach
Proc. IEEE4
2009 Panoptic DVS: A fine-grained dynamic voltage scaling framework for energy scalable CMOS design
abstract
The energy efficiency of a CMOS architecture processing dynamic workloads directly affects its ability to provide long battery lifetimes while maintaining required application performance. Existing scalable architecture design approaches are often limited in scope, focusing either only on circuit-level optimizations or architectural adaptations individually. In this paper, we propose a circuit/architecture co-design methodology called Panoptic Dynamic Voltage Scaling (PDVS) that makes more efficient use of common circuit structures and algorithm-level processing rate control. PDVS expands upon prior work by using multiple component-level PMOS header switches to enable fine-grained rate control, allowing efficient dithering among statically scheduled algorithms with sub-block energy savings. This way, PDVS is able to achieve a wide variety of processing rates to match incoming workload as closely as possible, while each iteration takes less energy to process than on architectures with coarser levels of rate control. Measurements taken from a fabricated 90 nm test chip characterize both savings and overheads and are used to inform PDVS synthesis decisions. Results show that PDVS consumes up to 34% and 44% less energy than Multi-VDD and Single-VDD systems, respectively.
Mateja Putic, Liang Di, Benton H. Calhoun, John C. Lach
ICCD1
2008 Power switch characterization for fine-grained dynamic voltage scaling
abstract
Dynamic voltage scaling (DVS) provides power savings for systems with varying performance requirements. One low overhead implementation of DVS uses PMOS power switches to connect DVS blocks to one of the available VDDsupplies. While power switches have been analyzed extensively for leakage power gating, proper design of power switches for DVS is less well understood. This paper characterizes power switches for DVS in terms of VDD-switching delay and VDD-switching energy. We show the impact of these switching overheads on a novel fine-grained DVS architecture and present an RC model that allows fast estimation of the overhead. Measurements of a DVS multiplier and adder on a 90 nm CMOS test chip validate the model. Our model and measurements confirm that power switched DVS can provide sufficiently low overhead to give energy savings with only one clock cycle spent at a lower voltage, making this approach a flexible and enticing option for embedded portable systems.
Liang Di, Mateja Putic, John C. Lach, Benton H. Calhoun
ICCD2