Archive of posts tagged: Machine Learning
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...
The New Bottlenecks of ML Training: A Storage Perspective
Machine Learning (ML), specifically Deep Neural Networks (DNNs), is stressing storage systems in new ways, moving the training bottleneck to the data ingestion phase, rather than the actual learning phase. Training these models is data-hungry, resource-intensive, and...
Catalyzing Computing Podcast – Computer Architecture with Mark D. Hill
[Editor’s Note: This article originally appeared on the CCC blog (part 1 and part 2) and is re-posted here with permission.] A new episode of the Computing Community Consortium‘s (CCC) official podcast, Catalyzing Computing, is now available. In this episode, Khari...Subscribe
