8 citations · 16 across the 8 of their papers we have counts for
5 papers · 1 filter
Revisiting Block-based Quantisation: What is Important for Sub-8-bit LLM Inference?
Cheng Zhang, Jianyi Cheng, Ilia Shumailov +2
The inference of Large language models (LLMs) requires immense computation and memory resources. To curtail these costs, quantisation has merged as a promising solution, but existi…
GSA to HDL: Towards principled generation of dynamically scheduled circuits
Aditya Rajagopal, Diederik Adriaan Vink, Jianyi Cheng +1
High-level synthesis (HLS) refers to the automatic translation of a software program written in a high-level language into a hardware design. Modern HLS tools have moved away from…
SEER: Super-Optimization Explorer for HLS using E-graph Rewriting with MLIR
Jianyi Cheng, Samuel Coward, Lorenzo Chelini +2
High-level synthesis (HLS) is a process that automatically translates a software program in a high-level language into a low-level hardware description. However, the hardware desig…
PASS: Exploiting Post-Activation Sparsity in Streaming Architectures for CNN Acceleration
Alexander Montgomerie-Corcoran, Zhewen Yu, Jianyi Cheng +1
With the ever-growing popularity of Artificial Intelligence, there is an increasing demand for more performant and efficient underlying hardware. Convolutional Neural Networks (CNN…
A Dataflow Compiler for Efficient LLM Inference using Custom Microscaling Formats
Jianyi Cheng, Cheng Zhang, Zhewen Yu +3
Model quantization represents both parameters (weights) and intermediate values (activations) in a more compact format, thereby directly reducing both computational and memory cost…