4 papers · 1 filter
BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration
Yuzong Chen, Ahmed F. AbouElhamayed, Xilai Dai +4
Large language models (LLMs) have demonstrated remarkable performance across various machine learning tasks. Yet the substantial memory footprint of LLMs significantly hinders thei…
BBS: Bi-directional Bit-level Sparsity for Deep Learning Acceleration
Yuzong Chen, Jian Meng, Jae-sun Seo +1
Bit-level sparsity methods skip ineffectual zero-bit operations and are typically applicable within bit-serial deep learning accelerators. This type of sparsity at the bit-level is…
Kratos: An FPGA Benchmark for Unrolled DNNs with Fine-Grained Sparsity and Mixed Precision
Xilai Dai, Yuzong Chen, Mohamed S. Abdelfattah
FPGAs offer a flexible platform for accelerating deep neural network (DNN) inference, particularly for non-uniform workloads featuring fine-grained unstructured sparsity and mixed…
Learning from Students: Applying t-Distributions to Explore Accurate and Efficient Formats for LLMs
Jordan Dotzel, Yuzong Chen, Bahaa Kotb +5
The increasing size of large language models (LLMs) traditionally requires low-precision integer formats to meet strict latency and power demands. Yet recently, alternative formats…