Scalable Deep Neural Network Accelerator Design and Methodology

Machine learning systems are being widely deployed across billions of edge devices and datacenter across the world. At the same time, in the absence of Moore’s Law and Dennard scaling, we rely on building vertically integrated systems with domain-specific accelerators to improve the system performance and efficiency. In this talk, I will describe our recent work on building scalable and efficient hardware that delivers real-time and robust performance across diverse deployment scenarios through joint hardware-software optimizations. I will conclude my talk by describing ongoing efforts toward building next-generation computing platforms for real-time machine learning.

Speaker: Sophia Shao, UC Berkeley