Archive of posts tagged: Machine Learning
Multi-Modal On-Device AI: Heterogeneous Computing Once More?
What is Multi-modal AI? Prior research on developing on-device AI solutions have primarily focused on improving the TOPS (Tera Operations Per Second) or TOPS/Watt of AI accelerators by leveraging sparsity, quantization, or efficient neural network architectures...
A Case for Optical Deep Neural Networks
Deep Neural Networks have been a major focus for computer architects in the recent past due to the massive parallelism available in computation, combined with the massive amount of data re-use. While the proposed architectures have inspired industry innovations such...
Architecture Innovation Accelerates Artificial Intelligence
[Editor’s Note: This article originally appeared on the blogs of the HLF and the CCC and is re-posted here with permission.] As part of the first day of the Virtual Heidelberg Laureate Forum (HLF) David A. Patterson, who won the 2017 ACM A.M Turing Award “for...
Building Performance Scalable and Composable Machine Learning Accelerators
To meet machine learning (ML) practitioners’ insatiable demand for higher processing power, computer architects have been at the forefront of developing accelerated computing solutions for ML that fundamentally changed the landscape of the computing industry. Given...Subscribe
