Session
Breaking Barriers: Compute-in-Memory for Transformer Acceleration
Session Chairs
DescriptionThis session highlights groundbreaking innovations in Compute-in-Memory (CIM) architectures designed to accelerate large-scale transformer models and alleviate data transfer bottlenecks. It covers various optimization strategies, from CIM-based solutions for attention computation in large language models (LLMs) to efficient memory management for long-context inference. The session delves into software-hardware co-design techniques, including irregular attention sparsity, outlier-aware quantization, Mixture-of-Experts (MoE) approaches, 3D hybrid bonding, and novel asynchronous execution methods.
Event Type
Research Manuscript
TimeWednesday, June 253:30pm - 5:30pm PDT
Location3001, Level 3
Design
DES2B: In-memory and Near-memory Computing Architectures, Applications and Systems
Presentations


