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Found 8 papers, 3 papers with code
Microscaling Data Formats for Deep Learning
Narrow bit-width data formats are key to reducing the computational and storage costs of modern deep learning applications.
With Shared Microexponents, A Little Shifting Goes a Long Way
This paper introduces Block Data Representations (BDR), a framework for exploring and evaluating a wide spectrum of narrow-precision formats for deep learning.
Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point
In this paper, we explore the limits of Microsoft Floating Point (MSFP), a new class of datatypes developed for production cloud-scale inferencing on custom hardware.
Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations
The proposed approach is applicable to a variety of DNN architectures and significantly reduces the computational cost of DNN execution with almost no accuracy loss.
OverQ: Opportunistic Outlier Quantization for Neural Network Accelerators
Specialized hardware for handling activation outliers can enable low-precision neural networks, but at the cost of nontrivial area overhead.
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
The majority of existing literature focuses on training quantized DNNs, while this work examines the less-studied topic of quantizing a floating-point model without (re)training.
Building Efficient Deep Neural Networks with Unitary Group Convolutions
UGConvs generalize two disparate ideas in CNN architecture, channel shuffling (i. e. ShuffleNet) and block-circulant networks (i. e. CirCNN), and provide unifying insights that lead to a deeper understanding of each technique.
Binarized Convolutional Neural Networks with Separable Filters for Efficient Hardware Acceleration
State-of-the-art convolutional neural networks are enormously costly in both compute and memory, demanding massively parallel GPUs for execution.
