Timeloop/Accelergy Tutorial (Hands-on Session) @ ISCA 2020
Tutorial Website: http://accelergy.mit.edu/isca20_tutor...
This video focuses on hands-on exercises. Introduction and Overview Lecture at • Timeloop/Accelergy Tutorial @ ISCA 2020
Outline:
0:00 - Introduction
30:52 - Timeloop Exercises
1:16:58 - Accelergy Exercises
Timeloop Slides: http://accelergy.mit.edu/isca2020/202...
Accelergy Slides: http://accelergy.mit.edu/isca2020/202...
Deep neural networks have emerged as the key approach for solving a wide range of complex problems. To provide high performance and energy efficiency to this class of computation and memory-intensive applications, many DNN accelerators have been proposed in recent years. In order to systematically evaluate arbitrary DNN accelerator designs, we need to have an infrastructure that is able to:
Describe a wide range of architectures
Find optimal mappings for a wide range of workloads onto the architecture
Accurately predict energy for a range of accelerator designs
Handle a wide range of technologies
In this tutorial, we will present two integrated tools that enable rapid evaluation of DNN accelerators:
Mapping exploration with Timeloop: http://accelergy.mit.edu/timeloop.pdf
Energy estimation with Accelergy: http://accelergy.mit.edu/paper.pdf
Tutorial Organizers: Angshuman Parashar (NVIDIA), Yannan Nellie Wu (MIT), Po-An Tsai (NVIDIA), Vivienne Sze (MIT), Joel S. Emer (NVIDIA, MIT)