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Found 9 papers, 2 papers with code
FP8 Formats for Deep Learning
FP8 is a natural progression for accelerating deep learning training inference beyond the 16-bit formats common in modern processors.
Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation
Quantization techniques can reduce the size of Deep Neural Networks and improve inference latency and throughput by taking advantage of high throughput integer instructions.
DPRed: Making Typical Activation and Weight Values Matter In Deep Learning Computing
The per group precisions are selected statically for the weights and dynamically by hardware for the activations.
Bit-Tactical: Exploiting Ineffectual Computations in Convolutional Neural Networks: Which, Why, and How
We show that, during inference with Convolutional Neural Networks (CNNs), more than 2x to $8x ineffectual work can be exposed if instead of targeting those weights and activations that are zero, we target different combinations of value stream properties.
Tartan: Accelerating Fully-Connected and Convolutional Layers in Deep Learning Networks by Exploiting Numerical Precision Variability
Experiments on image classification CNNs show that on average across all networks studied, TRT outperforms a state-of-the-art bit-parallel accelerator by 1:90x without any loss in accuracy while it is 1:17x more energy efficient.
Loom: Exploiting Weight and Activation Precisions to Accelerate Convolutional Neural Networks
LM can trade-off accuracy for additional improvements in execution performance and energy efficiency and compares favorably to an accelerator that targeted only activation precisions.
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 computation to offer performance that is proportional to the fixed-point precision of the activation values.
Cnvlutin2: Ineffectual-Activation-and-Weight-Free Deep Neural Network Computing
We also present a modified organization that detects the activations that are deemed as ineffectual while fetching them from memory.
Reduced-Precision Strategies for Bounded Memory in Deep Neural Nets
A diverse set of CNNs is analyzed showing that compared to a conventional implementation using a 32-bit floating-point representation for all layers, and with less than 1% loss in relative accuracy, the data footprint required by these networks can be reduced by an average of 74% and up to 92%.
