Electronic Design

Machine Learning at the Edge: Using High-Level Synthesis to Optimize Power and Performance

Create new power and memory efficient hardware architectures to meet next-generation machine learning hardware demands. Moving machine learning to the edge has critical requirements on power and ...
Phys.org

Quantum machine learning nears practicality as partial error correction reduces hardware demands

Imagine a future where quantum computers supercharge machine learning—training models in seconds, extracting insights from massive datasets and powering next-gen AI. That future might be closer than ...
Semiconductor Engineering

Overview of Machine Learning Algorithms Used In Hardware Security (TU Delft)

A new technical paper titled “A Survey on Machine Learning in Hardware Security” was published by researchers at TU Delft. “Hardware security is currently a very influential domain, where each year ...
Electronic Design

Machine Learning at the Edge: Using High-Level Synthesis to Optimize Power and Performance

Moving machine learning to the edge has critical requirements on power and performance. Using off-the-shelf solutions is not practical. CPUs are too slow, GPUs/TPUs are expensive and consume too much ...