Archive of posts tagged: Machine Learning
5 Guidelines for Research in ML for Systems
It was more than 10 years ago that I first started studying machine learning (ML). Serendipitously, I ended up leveraging ML techniques developed for biological sequence analysis for optimizing storage systems. In the recent past, I have focused on optimizing and...
From Application Specific to General Purpose (Again)
With Dennard scaling discontinued, application-specific hardware accelerators are ubiquitous in modern computers to offer more efficient task processing. Famous examples include Google’s Tensor Processing Units (TPUs) and Apple’s Neural Engines for...
Numerical Encoding for DNN Accelerators
DNN training is emerging as a popular compute-intensive workload. This blog post provides an overview of the recent research on numerical encoding formats for DNN training, and presents the Hybrid Block Floating-Point (HBFP) format which reduces silicon provisioning...
An Academic’s Attempt to Clear the Fog of the Machine Learning Accelerator War
At its core, all engineering is science optimized (or perverted) by economics. As academics in computer science and engineering, we have a symbiotic relationship with industry. Still, it is often necessary for us to peel back the marketing noise and understand...Subscribe
