3 papers
cs.AR2024
Efficient Sparse Processing-in-Memory Architecture (ESPIM) for Machine Learning Inference
Mingxuan He, Mithuna Thottethodi, T. N. Vijaykumar
Emerging machine learning (ML) models (e.g., transformers) involve memory pin bandwidth-bound matrix-vector (MV) computation in inference. By avoiding pin crossings, processing in…
cs.AR2024
QED: Scalable Verification of Hardware Memory Consistency
Gokulan Ravi, Xiaokang Qiu, Mithuna Thottethodi +1
Memory consistency model (MCM) issues in out-of-order-issue microprocessor-based shared-memory systems are notoriously non-intuitive and a source of hardware design bugs. Prior har…
cs.AR2024
NetSmith: An Optimization Framework for Machine-Discovered Network Topologies
Conor Green, Mithuna Thottethodi
Over the past few decades, network topology design for general purpose, shared memory multicores has been primarily driven by human experts who use their insights to arrive at netw…