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 ...
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 ...
Seeking Alpha

Siemens simplifies development of AI accelerators for advanced system-on-chip designs with Catapult AI NN

Catapult AI NN offers software engineers a comprehensive solution to synthesize AI Neural Nets Enables software development teams to seamlessly translate AI models designed in Python into ...
AppleInsider

Apple Silicon machine learning code may become more easily portable to Nvidia hardware

A project is trying to cut the cost of making machine learning applications for Nvidia hardware, by developing on an Apple Silicon Mac and exporting it to CUDA. Machine learning is costly to enter, in ...
InfoWorld

IBM sets up a machine learning pipeline for z/OS

z System users with data behind their firewalls can now access IBM's training and deployment system for machine learning, packaged for convenience If you’re intrigued by IBM’s Watson AI as a service, ...
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 ...