research
∙
04/28/2022
Schrödinger's FP: Dynamic Adaptation of Floating-Point Containers for Deep Learning Training
We introduce a software-hardware co-design approach to reduce memory tra...
share
research
∙
03/23/2022
Mokey: Enabling Narrow Fixed-Point Inference for Out-of-the-Box Floating-Point Transformer Models
Increasingly larger and better Transformer models keep advancing state-o...
research
∙
01/21/2022
APack: Off-Chip, Lossless Data Compression for Efficient Deep Learning Inference
Data accesses between on- and off-chip memories account for a large frac...
research
∙
10/15/2020
FPRaker: A Processing Element For Accelerating Neural Network Training
We present FPRaker, a processing element for composing training accelera...
research
∙
09/01/2020
TensorDash: Exploiting Sparsity to Accelerate Deep Neural Network Training and Inference
TensorDash is a hardware level technique for enabling data-parallel MAC ...
research
∙
05/10/2018
Laconic Deep Learning Computing
We motivate a method for transparently identifying ineffectual computati...
research
∙
03/09/2018
Bit-Tactical: Exploiting Ineffectual Computations in Convolutional Neural Networks: Which, Why, and How
We show that, during inference with Convolutional Neural Networks (CNNs)...
research
∙
11/30/2016