Improving GPU Energy Efficiency With Component-Level Power Management (AMD)


Researchers from AMD released “CompPow: A Case for Component-level GPU Power Management”. Abstract “The ever increasing demand for ML-driven intelligence in a wide spectrum of domains has led to ubiquity of GPUs. At the same time, GPUs are notorious for their power consumption needs and often dominate power allocation in a typical ML datacenter. While datacenter-level power opti... » read more

3D Heterogeneous Integration System To Accelerate LLMs (Georgia Tech)


A new technical paper titled "A3D-MoE: Acceleration of Large Language Models with Mixture of Experts via 3D Heterogeneous Integration" was published by researchers at Georgia Institute of Technology. Abstract "Conventional large language models (LLMs) are equipped with dozens of GB to TB of model parameters, making inference highly energy-intensive and costly as all the weights need to be ... » 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

Software-Hardware Co-Design Becomes Real


For the past 20 years, the industry has sought to deploy hardware/software co-design concepts. While it is making progress, software/hardware co-design appears to have a much brighter future. In order to understand the distinction between the two approaches, it is important to define some of the basics. Hardware/software co-design is essentially a bottom-up process, where hardware is deve... » read more