Scheduling Multi-Model AI Workloads On Heterogeneous MCM Accelerators (UC Irvine)

A technical paper titled “SCAR: Scheduling Multi-Model AI Workloads on Heterogeneous Multi-Chiplet Module Accelerators” was published by researchers at University of California Irvine. Abstract: "Emerging multi-model workloads with heavy models like recent large language models significantly increased the compute and memory demands on hardware. To address such increasing demands, designin... » read more

Ferroelectric Memory-Based IMC for ML Workloads

A new technical paper titled "Ferroelectric capacitors and field-effect transistors as in-memory computing elements for machine learning workloads" was published by researchers at Purdue University. Abstract "This study discusses the feasibility of Ferroelectric Capacitors (FeCaps) and Ferroelectric Field-Effect Transistors (FeFETs) as In-Memory Computing (IMC) elements to accelerate mach... » read more

Chiplets For Generative AI Workloads: Challenges in both HW and SW

A new technical paper titled "Challenges and Opportunities to Enable Large-Scale Computing via Heterogeneous Chiplets" was published by researchers at University of Pittsburgh, Lightelligence, and Meta. Abstract "Fast-evolving artificial intelligence (AI) algorithms such as large language models have been driving the ever-increasing computing demands in today's data centers. Heterogeneous c... » read more