Blog Review: Dec. 30
Cadence’s Paul McLellan considers what the next ten years will look like for the RISC-V ISA with an expanding software ecosystem and increasing number of commercial and open cores available.
Siemens EDA’s Harry Foster checks out the languages and libraries being used to design and verify FPGAs and how they’ve changed over the last several years.
Synopsys’ Jonathan Knudsen contends that IT and application security are more than just buying equipment and installing it in your network or buying a code analysis tool, and that security needs to be part of every phase of development.
Arm’s Allaukik Abhishek explains that intrusion detection and prevention for heterogenous IoT networks is urgently needed and proposes a method using deep learning and device fingerprinting to flag anomalous behavior.
A Rambus writer takes a look back at the semiconductor industry in 2020 with a big boost in memory chip sales, more focus on AI and edge infrastructure, and a few mega-acquisitions.
Ansys’ David Schneider looks at ways to allow cooperation in multiphysics simulation and multidisciplinary optimization through automation and publishing of workflows for repetitive tasks and simulation process data management.
SEMI’s Serena Brischetto chats with Tom Doyle of Aspinity about improving power efficiency of IoT devices by performing machine learning on raw analog sensor data.
Technology Editor Brian Bailey recounts how getting sound to your ear wirelessly has been a long journey.
Cadence’s Frank Schirrmeister foresees that early and continuous integration is poised to shift development efforts left and to reduce overall development effort significantly.
Codasip’s Roddy Urquhart explains what IP licensees are really paying for.
OneSpin’s Rob van Blommestein wants you to put your skills to the test with a new puzzle.
Mentor’s James Paris and Armen Asatryan dig into why the ability to transfer data across the entire design flow is crucial to efficient design methodologies.
Synopsys’s Arun Venkatachar and Stelios Diamantidis predict architecture-aware design tools, reinforcement learning, and more trends for the next year in AI.