Anomaly detection (AD) task corresponds to identifying the true anomalies among a given set of data instances. AD algorithms score the data instances and produce a ranked list of candidate anomalies. The ranked list of anomalies is then analyzed by a human to discover the true anomalies. Ensemble of tree-based anomaly detectors trained in an unsupervised manner and scoring based on uniform weights for ensembles are shown to work well in practice. However, the manual process of analysis can be laborious for the human analyst when the number of false-positives is very high. Therefore, in many real-world AD applications including computer security and fraud prevention, the anomaly detector must be configurable by the human analyst to minimize the effort on false positives. One important way to configure the detector is by providing true labels (nominal or anomaly) for a few instances. Recent work on active anomaly discovery has shown that greedily querying the top-scoring instance and tuning the weights of ensembles based on label feedback allows us to quickly discover true anomalies. This paper makes four main contributions to improve the state-of-the-art in anomaly discovery using tree-based ensembles. First, we provide an important insight that explains the practical successes of unsupervised tree-based ensembles and active learning based on greedy query selection strategy. We also show empirical results on real-world data to support our insights and theoretical analysis to support active learning. Second, we develop a novel batch active learning algorithm to improve the diversity of discovered anomalies based on a formalism called compact description to describe the discovered anomalies. Third, we develop a novel active learning algorithm to handle streaming data setting. We present a data drift detection algorithm that not only detects the drift robustly, but also allows us to take corrective actions to adapt the anomaly detector in a principled manner. Fourth, we present extensive experiments to evaluate our insights and our tree-based active anomaly discovery algorithms in both batch and streaming data settings. Our results show that active learning allows us to discover significantly more anomalies than state-of-the-art unsupervised baselines, our batch active learning algorithm discovers diverse anomalies, and our algorithms under the streaming-data setup are competitive with the batch setup.

A huge number of edge applications including self-driving cars, mobile health, robotics, and augmented reality / virtual reality are enabled by deep neural networks (DNNs). Currently, much of this computation for these applications happens in the cloud, but there are several good reasons to perform the processing on local edge platforms such as smartphones: improved accessibility to different parts of the world, low latency, and data privacy. In this paper, we present a general hardware and software co-design framework for energy-efficient edge AI for both simple classification and structured output prediction tasks (e.g., 3D shapes from images). This framework relies on two key ideas. First, we design a space of DNNs of increasing complexity (coarse to fine) and perform input-specific adaptive inference by selecting a DNN of appropriate complexity depending on the hardness of input examples. Second, we execute the selected DNN on the target edge platform using a resource management policy to save energy. We also provide instantiations of our co-design framework for three qualitatively different problem settings: convolutional neural networks for image classification, graph convolutional networks for predicting 3D shapes from images, and generative adversarial networks on photo-realistic unconditional image generation. Our experiments on real-world benchmarks and mobile platforms show the effectiveness of our co-design framework in achieving significant gain in energy with little to no loss in accuracy of predictions.

We consider the problem of multi-objective (MO) blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto set of solutions while minimizing the number of function evaluations. For example, in hardware design optimization, we need to find the designs that trade-off performance, energy, and area overhead using expensive simulations. We propose a novel uncertainty-aware search framework referred to as USeMO to efficiently select the sequence of inputs for evaluation to solve this problem. The selection method of USeMO consists of solving a cheap MO optimization problem via surrogate models of the true functions to identify the most promising candidates and picking the best candidate based on a measure of uncertainty. We also provide theoretical analysis to characterize the efficacy of our approach. Our experiments on several synthetic and six diverse real-world benchmark problems show that USeMO consistently outperforms the state-of-the-art algorithms.

We consider the task of predicting 3D object shapes from color images on mobile platforms, which has many real-world applications including augmented reality (AR), virtual reality (VR), and robotics. Recent work has developed a Graph Convolution Network (GCN) based approach to produce 3D object shapes in the form of a triangular mesh of increasing polycount (no. of triangles in the mesh). In this paper, we propose a novel approach to trade-off polycount of a 3D object shape for the energy consumed at run-time called Polycount-Energy Trade-off networks (PETNet). The key idea behind PETNets is to design an architecture of increasing complexity with a comparator module and leveraging the pre-trained GCN to perform input-specific adaptive predictions. We perform experiments using pre-trained GCN on the ShapeNet dataset. Results show that with the optimized PETNets, we can get up to 20%-37% gain in energy for negligible loss (0.01 to 0.02) in accuracy, and provides a fine-grained control on performance when compared to a fixed level performance with the state-of-the-art Pixel2Mesh network.

Deep Neural Networks (DNNs) employed for image segmentation are computationally more expensive and complex compared to the ones used for classification. However, manycore architectures to accelerate the training of these DNNs are relatively unexplored. Resistive random-access memory (ReRAM)-based architectures offer a promising alternative to commonly used GPU-based platforms for training DNNs. However, due to their low-precision storage capability, these architectures cannot support all DNN layers and suffer from accuracy loss of the learned models. To address these challenges, we propose GRAMARCH, a heterogeneous architecture that combines the benefits of ReRAM and GPUs simultaneously by using a high-throughput 3D Network-on-Chip. Experimental results indicate that by suitably mapping DNN layers to processing elements, it is possible to achieve up to 53X better performance compared to conventional GPUs for image segmentation.

We consider the task of photo-realistic unconditional image generation (generate high quality, diverse samples that carry the same visual content as the image) on mobile platforms using Generative Adversarial Networks (GANs). In this paper, we propose a novel approach to trade-off image generation accuracy of a GAN for the energy consumed (compute) at run-time called Scale-Energy Trade-off GAN (SETGAN). GANs usually take a long time to train and consume a huge memory hence making it difficult to run on edge devices. The key idea behind SETGAN for an image generation task is for a given input image, we train a GAN on a remote server and use the trained model on edge devices. We use SinGAN, a single image unconditional generative model, that contains a pyramid of fully convolutional GANs, each responsible for learning the patch distribution at a different scale of the image. During the training process, we determine the optimal number of scales for a given input image and the energy constraint from target edge device. Results show that with the SETGAN's unique client-server based architecture, we were able to achieve 56% gain in energy for a loss of 3% to 12% SSIM accuracy. Also, with the parallel multi-scale training, we obtain around 4x gain in training time on the server.

Many real-world edge applications including object detection, robotics, and smart health are enabled by deploying deep neural networks (DNNs) on energy-constrained mobile platforms. In this article, we propose a novel approach to trade off energy and accuracy of inference at runtime using a design space called Learning Energy Accuracy Tradeoff Networks (LEANets). The key idea behind LEANets is to design classifiers of increasing complexity using pretrained DNNs to perform input-specific adaptive inference. The accuracy and energy consumption of the adaptive inference scheme depends on a set of thresholds, one for each classifier. To determine the set of threshold vectors to achieve different energy and accuracy tradeoffs, we propose a novel multiobjective optimization approach. We can select the appropriate threshold vector at runtime based on the desired tradeoff. We perform experiments on multiple pretrained DNNs including ConvNet, VGG-16, and MobileNet using diverse image classification datasets. Our results show that we get up to a 50% gain in energy for negligible loss in accuracy, and optimized LEANets achieve significantly better energy and accuracy tradeoff when compared to a state-of-the-art method referred to as Slimmable neural networks.

The growing needs of emerging applications has posed significant challenges for the design of optimized manycore systems. Network-on-Chip (NoC) enables the integration of a large number of processing elements (PEs) in a single die. To design optimized manycore systems, we need to establish suitable trade-offs among multiple objectives including power, performance, and thermal. Therefore, we consider multi-objective design space exploration (MO-DSE) problems arising in the design of NoC-enabled manycore systems: placement of PEs and communication links to optimize two or more objectives (e.g., latency, energy, and throughput). Existing algorithms to solve MO-DSE problems suffer from scalability and accuracy challenges as size of the design space and the number of objectives grow. In this paper, we propose a novel framework referred as Multi-Objective Optimistic Search (MOOS) that performs adaptive design space exploration using a data-driven model to improve the speed and accuracy of multi-objective design optimization process. We apply MOOS to design both 3D heterogeneous and homogeneous manycore systems using Rodinia, PARSEC, and SPLASH2 benchmark suites. We demonstrate that MOOS improves the speed of finding solutions compared to state-of-the-art methods by up to 13X while uncovering designs that are up to 20% better in terms of NoC. The optimized 3D manycore systems improve the EDP up to 38% when compared to 3D mesh-based designs optimized for the placement of PEs.

Deep neural networks have seen tremendous success for different modalities of data including images, videos, and speech. This success has led to their deployment in mobile and embedded systems for real-time applications. However, making repeated inferences using deep networks on embedded systems poses significant challenges due to constrained resources (e.g., energy and computing power). To address these challenges, we develop a principled co-design approach. Building on prior work, we develop a formalism referred as coarse-to-fine networks (C2F Nets) that allow us to employ classifiers of varying complexity to make predictions. We propose a principled optimization algorithm to automatically configure C2F Nets for a specified tradeoff between accuracy and energy consumption for inference. The key idea is to select a classifier on-the-fly whose complexity is proportional to the hardness of the input example: simple classifiers for easy inputs and complex classifiers for hard inputs. We perform comprehensive experimental evaluation using four different C2F Net architectures on multiple real-world image classification tasks. Our results show that optimized C2F Net can reduce the energy delay product by 27% to 60% with no loss in accuracy when compared to the baseline solution, where all predictions are made using the most complex classifier in C2F Net.