News
Study Of HW Acceleration for Neural Networks (Arizona State Univ.)
6+ hour, 41+ min ago (356+ words) A new technical paper titled "Hardware Acceleration for Neural Networks: A Comprehensive Survey" was published by researchers at Arizona State University. Abstract "Neural networks have become a dominant computational workload across cloud and edge platforms, but their rapid growth in…...
Scalable AI/ML Method For Improved MTJ Performance (UT Austin, TSMC, TDK Headway)
9+ hour, 53+ min ago (377+ words) A new technical paper titled "LEAD: Literature Enhanced Ab Initio Discovery of Nitride Dusting Layers for Enhanced Tunnel Magnetoresistance and Lower Resistance Magnetic Tunnel Junctions" was published by researchers at University of Texas at Austin, TSMC, and TDK Headway Technologies…...
Reliability Extension Architecture For Cost-Effective HBM (RPI, ScaleFlux, IBM TJ Watson)
1+ day, 8+ hour ago (473+ words) A new technical paper titled "Making Strong Error-Correcting Codes Work Effectively for HBM in AI Inference" was published by researchers at Rensselaer Polytechnic Institute, ScaleFlux and IBM T.J. Watson Research Center. Abstract "LLM inference is increasingly memory bound, and HBM cost…...
Blog Review: Dec. 24
1+ week, 2+ day ago (515+ words) Software-defined vehicles spur collaboration, disruption, and much more code; Lava Lamp entropy; AI for PHYs; fall detection; water risk. Cadence's Jakob Engblom shares highlights from the recent SDV Europe conference, including why software-defined vehicles will require much closer, faster collaboration…...
Transforming Data Management In EDA: Preparing For The AI Era
1+ week, 3+ day ago (182+ words) Ensuring data moves smoothly across multiple disciplines, tools, and globally distributed teams. Many engineers discover the importance of data management through hands-on challenges in their own workflows. For example, front-end and analog designers often build custom scripts, tools, and automation…...
Accelerating Semiconductor Innovation Through Machine Learning-Driven Modeling
1+ week, 3+ day ago (301+ words) Using AI and machine learning as transformative solutions for semiconductor device modeling and parameter extraction. Accelerating Semiconductor Innovation Through Machine Learning-Driven Modeling Using AI and machine learning as transformative solutions for semiconductor device modeling and parameter extraction. The semiconductor industry…...
LLM- Based Techniques To Support Behavior-Driven Development For HW Design (U. of Bremen, DFKI)
1+ week, 4+ day ago (210+ words) A new technical paper titled "LLM-based Behaviour Driven Development for Hardware Design" was published by researchers at University of Bremen/DFKI. Abstract "Test and verification are essential activities in hardware and system design, but their complexity grows significantly with increasing…...
Data-Centric ML Compiler For PIM (U. of Toronto, Barcelona Supercomputing Center, ETH Zurich, Max Planck)
2+ week, 10+ hour ago (272+ words) A new technical paper titled "A Tensor Compiler for Processing-In-Memory Architectures" was published by researchers at University of Toronto, Barcelona Supercomputing Center, ETH Zurich, and the Max Planck Institute for Software Systems. Abstract "Processing-In-Memory (PIM) devices integrated with high-performance Host…...
Faster Mask Synthesis With GPUs
2+ week, 1+ day ago (601+ words) Accelerating computational lithography to enable more advanced optimizations at leading-edge nodes. The post Faster Mask Synthesis With GPUs appeared first on Semiconductor Engineering. Industry background: AI is reshaping compute demand AI is driving an unprecedented surge in compute demand across…...
Benefits And Limits Of Using ML For Materials Discovery
2+ week, 1+ day ago (633+ words) Humans are still necessary, and no tool does everything, but the potential is significant. Machine learning tools can accelerate all stages of materials discovery, from initial screening to process development. Whether the goal is to identify new applications for known…...