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As the size of Deep Neural Networks (DNNs) continues to grow to increase accuracy and solve more complex problems, their energy footprint also scales. Weight pruning reduces DNN model size and the computation by removing redundant weights. However, we implemented weight pruning for several popular networks on a variety of hardware platforms and observed surprising results. For many networks, the network...
Medical imaging provides physicians with the ability to generate 3D images of the human body in order to detect and diagnose a wide variety of ailments. Making medical imaging portable and more accessible provides a unique set of challenges. In order to increase portability, the power consumed in image acquisition - currently the most power-consuming activity in an imaging device - must be dramatically...
Single-thread performance, power efficiency and reliability are critical design challenges of future multicore systems. Although point solutions have been proposed to address these issues, a more fundamental change to the fabric of multicore systems is necessary to seamlessly combat these challenges. Towards this end, this paper proposes CoreGenesis, a dynamically adaptive multiprocessor fabric that...
Modern graphics processing units (GPUs) combine large amounts of parallel hardware with fast context switching among thousands of active threads to achieve high performance. However, such designs do not translate well to mobile environments where power constraints often limit the amount of hardware. In this work, we investigate the use of prefetching as a means to increase the energy efficiency of...
We describe the implementation and silicon measurement results from a Razor-based hardware loop-accelerator (RZLA), implementing the Sobel edge-detection algorithm. We demonstrate robust operation with a large Dynamic Voltage Scaling (DVS) range achieved using 50% of the clock-period for timing-speculation. At 1GHz operating frequency, Razor DVS enables 34% energy-efficiency improvement on a per-device...
The rapid advancements in the computational capabilities of the graphics processing unit (GPU) as well as the deployment of general programming models for these devices have made the vision of a desktop supercomputer a reality. It is now possible to assemble a system that provides several TFLOPs of performance on scientific applications for the cost of a high-end laptop computer. While these devices...
The rapid advancements in the computational capabilities of the graphics processing unit (GPU) as well as the deployment of general programming models for these devices have made the vision of a desktop supercomputer a reality. It is now possible to assemble a system that provides several TFLOPs of performance on scientific applications for the cost of a high-end laptop computer. While these devices...
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