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Found 4 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.
FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference
Deep learning models typically use single-precision (FP32) floating point data types for representing activations and weights, but a slew of recent research work has shown that computations with reduced-precision data types (FP16, 16-bit integers, 8-bit integers or even 4- or 2-bit integers) are enough to achieve same accuracy as FP32 and are much more efficient.
Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications
The application of deep learning techniques resulted in remarkable improvement of machine learning models.
Glow: Graph Lowering Compiler Techniques for Neural Networks
The high-level intermediate representation allows the optimizer to perform domain-specific optimizations.
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