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.

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 Tradeoff 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.

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