125 citations · 361 across the 46 of their papers we have counts for
5 papers · 1 filter
NVCell: Standard Cell Layout in Advanced Technology Nodes with Reinforcement Learning
Haoxing Ren, Matthew Fojtik, Brucek Khailany
High quality standard cell layout automation in advanced technology nodes is still challenging in the industry today because of complex design rules. In this paper we introduce an…
LNS-Madam: Low-Precision Training in Logarithmic Number System using Multiplicative Weight Update
Jiawei Zhao, Steve Dai, Rangharajan Venkatesan +6
Representing deep neural networks (DNNs) in low-precision is a promising approach to enable efficient acceleration and memory reduction. Previous methods that train DNNs in low-pre…
Softermax: Hardware/Software Co-Design of an Efficient Softmax for Transformers
Jacob R. Stevens, Rangharajan Venkatesan, Steve Dai +2
Transformers have transformed the field of natural language processing. This performance is largely attributed to the use of stacked self-attention layers, each of which consists o…
Verifying High-Level Latency-Insensitive Designs with Formal Model Checking
Steve Dai, Alicia Klinefelter, Haoxing Ren +4
Latency-insensitive design mitigates increasing interconnect delay and enables productive component reuse in complex digital systems. This design style has been adopted in high-lev…
VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference
Steve Dai, Rangharajan Venkatesan, Haoxing Ren +3
Quantization enables efficient acceleration of deep neural networks by reducing model memory footprint and exploiting low-cost integer math hardware units. Quantization maps floati…