4 citations · 5 across the 2 of their papers we have counts for
2 papers
cs.AR2024★ 4 cited
tubGEMM: Energy-Efficient and Sparsity-Effective Temporal-Unary-Binary Based Matrix Multiply Unit
Prabhu Vellaisamy, Harideep Nair, Joseph Finn +7
General Matrix Multiplication (GEMM) is a ubiquitous compute kernel in deep learning (DL). To support energy-efficient edge-native processing, new GEMM hardware units have been pro…
cs.AR2024★ 1 cited
Commercial Evaluation of Zero-Skipping MAC Design for Bit Sparsity Exploitation in DL Inference
Harideep Nair, Prabhu Vellaisamy, Tsung-Han Lin +3
General Matrix Multiply (GEMM) units, consisting of multiply-accumulate (MAC) arrays, perform bulk of the computation in deep learning (DL). Recent work has proposed a novel MAC de…