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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...
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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...
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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...
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10/15/2020
FPRaker: A Processing Element For Accelerating Neural Network Training
We present FPRaker, a processing element for composing training accelera...
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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 ...
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05/08/2020
GOBO: Quantizing Attention-Based NLP Models for Low Latency and Energy Efficient Inference
Attention-based models have demonstrated remarkable success in various n...
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02/08/2020
BitPruning: Learning Bitlengths for Aggressive and Accurate Quantization
Neural networks have demonstrably achieved state-of-the art accuracy usi...
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10/15/2019
Training CNNs faster with Dynamic Input and Kernel Downsampling
We reduce training time in convolutional networks (CNNs) with a method t...
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05/10/2018
Laconic Deep Learning Computing
We motivate a method for transparently identifying ineffectual computati...
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04/17/2018
DPRed: Making Typical Activation Values Matter In Deep Learning Computing
We show that selecting a fixed precision for all activations in Convolut...
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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)...
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07/27/2017
Tartan: Accelerating Fully-Connected and Convolutional Layers in Deep Learning Networks by Exploiting Numerical Precision Variability
Tartan (TRT), a hardware accelerator for inference with Deep Neural Netw...
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06/23/2017
Loom: Exploiting Weight and Activation Precisions to Accelerate Convolutional Neural Networks
Loom (LM), a hardware inference accelerator for Convolutional Neural Net...
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06/01/2017
Dynamic Stripes: Exploiting the Dynamic Precision Requirements of Activation Values in Neural Networks
Stripes is a Deep Neural Network (DNN) accelerator that uses bit-serial ...
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11/30/2016
Memory Controller Design Under Cloud Workloads
This work studies the behavior of state-of-the-art memory controller des...
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11/17/2015