A Portable, Fast, DCT-based Compressor for AI Accelerators
Milan Shah, Xiaodong Yu, Sheng Di, Michela Becchi, Franck Cappello
Abstract
Lossy compression can be an effective tool in AI training and inference to reduce memory requirements, storage footprint, and in some cases, execution time. With the rise of novel architectures designed to accelerate AI workloads, compression can continue to serve these purposes, but must be adapted to the new accelerators. Due to programmability and architectural differences, existing lossy compressors cannot be directly ported to and are not optimized for any AI accelerator, thus requiring new compression designs.
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