no code implementations • 30 Jun 2022 • Ahmet Inci, Siri Garudanagiri Virupaksha, Aman Jain, Ting-Wu Chin, Venkata Vivek Thallam, Ruizhou Ding, Diana Marculescu
As the machine learning and systems communities strive to achieve higher energy-efficiency through custom deep neural network (DNN) accelerators, varied precision or quantization levels, and model compression techniques, there is a need for design space exploration frameworks that incorporate quantization-aware processing elements into the accelerator design space while having accurate and fast power, performance, and area models.
no code implementations • 20 May 2022 • Ahmet Inci, Siri Garudanagiri Virupaksha, Aman Jain, Venkata Vivek Thallam, Ruizhou Ding, Diana Marculescu
We also show that the proposed lightweight processing elements (LightPEs) consistently achieve Pareto-optimal results in terms of accuracy and hardware-efficiency.
no code implementations • 17 May 2022 • Ahmet Inci, Siri Garudanagiri Virupaksha, Aman Jain, Venkata Vivek Thallam, Ruizhou Ding, Diana Marculescu
As the machine learning and systems community strives to achieve higher energy-efficiency through custom DNN accelerators and model compression techniques, there is a need for a design space exploration framework that incorporates quantization-aware processing elements into the accelerator design space while having accurate and fast power, performance, and area models.