Analog: Avoid Or Embrace?


We live in an analog world, but digital processing has proven quicker, cheaper and easier. Moving digital data around is only possible while the physics of wires can be safely abstracted away enough to provide reliable communications. As soon as a signal passes off-chip, the analog domain reasserts control for modern systems. Each of those transitions requires a data converter. The usage ... » read more

Bridging Math And Engineering In ML


Steve Roddy, vice president of products for Arm’s Machine Learning Group, examines the intersection of high-level mathematics in the data science used in machine learning within area, speed, and power limitations, and how to bring these two worlds together with the least amount of disruption. » read more

From AI Algorithm To Implementation


Semiconductor Engineering sat down to discuss the role that EDA has in automating artificial intelligence and machine learning with Doug Letcher, president and CEO of Metrics; Daniel Hansson, CEO of Verifyter; Harry Foster, chief scientist verification for Mentor, a Siemens Business; Larry Melling, product management director for Cadence; Manish Pandey, Synopsys fellow; and Raik Brinkmann, CEO ... » read more

Building AI SoCs


Ron Lowman, strategic marketing manager at Synopsys, looks at where AI is being used and how to develop chips when the algorithms are in a state of almost constant change. That includes what moves to the edge versus the data center, how algorithms are being compressed, and what techniques are being used to speed up these chips and reduce power. https://youtu.be/d32jtdFwpcE    ... » read more