Survey: HW SW Co-Design Approaches Tailored to LLMs


A new technical paper titled "A Survey: Collaborative Hardware and Software Design in the Era of Large Language Models" was published by researchers at Duke University and Johns Hopkins University. Abstract "The rapid development of large language models (LLMs) has significantly transformed the field of artificial intelligence, demonstrating remarkable capabilities in natural language proce... » read more

Focus Shifts To Application-Specific Workloads


Experts At The Table: EDA has undergone numerous workflow changes over time. Different skill sets have come into play over the years, and at times this changed the definition of what it means to design at the system level. To work out what this means for designers today, and how it looks going forward, Semiconductor Engineering sat down with Michal Siwinski, chief marketing officer at Arteris; ... » read more

What’s Next In System-Level Design?


Experts At The Table: EDA has undergone numerous workflow changes over time. Different skill sets have come into play over the years, and at times this changed the definition of what it means to design at the system level. Semiconductor Engineering sat down to discuss what this means for designers today, and what the impact will be in the future, with Michal Siwinski, chief marketing officer at... » read more

Leveraging Large Language Models (LLMs) To Perform SW-HW Co-Design


A technical paper titled “On the Viability of using LLMs for SW/HW Co-Design: An Example in Designing CiM DNN Accelerators” was published by researchers at University of Notre Dame. Abstract: "Deep Neural Networks (DNNs) have demonstrated impressive performance across a wide range of tasks. However, deploying DNNs on edge devices poses significant challenges due to stringent power and com... » read more

HW-SW Co-Design Solution For Building Side-Channel-Protected ML Hardware


A technical paper titled "Hardware-Software Co-design for Side-Channel Protected Neural Network Inference" was published (preprint) by researchers at North Carolina State University and Intel. Abstract "Physical side-channel attacks are a major threat to stealing confidential data from devices. There has been a recent surge in such attacks on edge machine learning (ML) hardware to extract the... » read more