Tag: Hardware Accelerator

AI Machine Learning & Data Science Research

Google Brain’s EvoJAX Hardware-Accelerated Toolkit Significantly Improves Neuroevolutionary Computation

A Google Brain research team introduces EvoJAX, a JAX-based, scalable, general-purpose, hardware-accelerated neuroevolution toolkit that enables neuroevolution algorithms to work with neural networks running in parallel across multiple TPU/GPUs and achieves significant training speedups.

AI Machine Learning & Data Science Research

Google & UC Berkeley’s Data-Driven Offline Optimization Approach Significantly Boosts Hardware Accelerator Performance, Reduces Simulation Time by More Than 90%

A research team from Google Research and UC Berkeley proposes PRIME, an offline data-driven approach that can architect hardware accelerators without any form of simulations. Compared to state-of-the-art simulation-driven methods, PRIME achieves impressive performance improvements of up to 1.54× while reducing the total required simulation time by up to 99 percent.