Archive of posts tagged: Machine Learning
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...
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...Subscribe
