Blog Review: Nov. 4

Arm's Joshua Sowerby points to how to improve machine learning performance on mobile devices by using smart pruning to remove convolution filters from a network, reducing its size, complexity, and memory footprint. Mentor's Neil Johnson checks out how designers can write and verify RTL real-time using formal property checking in the style of test-driven development and why to give it a try. Cadence's Paul McLellan shares some highlights from the recent Linley Fall Processor Conference on the slowing of Moore's Law and key trends in AI acceleration in both the data center and at the edge. Synopsys' Arun Venkatachar looks at why IBM Research and Synopsys are collaborating to build AI-focused hardware and the progress that has already been made. Ansys' Rich Goldman and Peter Hallschmid consider the current state of photonics design with more sophisticated electronic-photonic flows and DFM capabilities, plus the major application areas seeing growing use of photonics.