1 citations · 3 across the 11 of their papers we have counts for
11 papers
From Loop Nests to Silicon: Mapping AI Workloads onto AMD NPUs with MLIR-AIR
Erwei Wang, Samuel Bayliss, Andra Bisca +19
General-purpose compilers abstract away parallelism, locality, and synchronization, limiting their effectiveness on modern spatial architectures. As modern computing architectures…
ITERA-LLM: Boosting Sub-8-Bit Large Language Model Inference via Iterative Tensor Decomposition
Keran Zheng, Yinting Huang, Zhewen Yu +1
Recent advancements in Large Language Models (LLMs) have demonstrated impressive capabilities as their scale expands to billions of parameters. Deploying these large-scale models o…
HASS: Hardware-Aware Sparsity Search for Dataflow DNN Accelerator
Zhewen Yu, Sudarshan Sreeram, Krish Agrawal +6
Deep Neural Networks (DNNs) excel in learning hierarchical representations from raw data, such as images, audio, and text. To compute these DNN models with high performance and ene…
SMOF: Streaming Modern CNNs on FPGAs with Smart Off-Chip Eviction
Petros Toupas, Zhewen Yu, Christos-Savvas Bouganis +1
Convolutional Neural Networks (CNNs) have demonstrated their effectiveness in numerous vision tasks. However, their high processing requirements necessitate efficient hardware acce…
AutoWS: Automate Weights Streaming in Layer-wise Pipelined DNN Accelerators
Zhewen Yu, Christos-Savvas Bouganis
With the great success of Deep Neural Networks (DNN), the design of efficient hardware accelerators has triggered wide interest in the research community. Existing research explore…
SATAY: A Streaming Architecture Toolflow for Accelerating YOLO Models on FPGA Devices
Alexander Montgomerie-Corcoran, Petros Toupas, Zhewen Yu +1
AI has led to significant advancements in computer vision and image processing tasks, enabling a wide range of applications in real-life scenarios, from autonomous vehicles to medi…
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…
Mixed-TD: Efficient Neural Network Accelerator with Layer-Specific Tensor Decomposition
Zhewen Yu, Christos-Savvas Bouganis
Neural Network designs are quite diverse, from VGG-style to ResNet-style, and from Convolutional Neural Networks to Transformers. Towards the design of efficient accelerators, many…
SVD-NAS: Coupling Low-Rank Approximation and Neural Architecture Search
Zhewen Yu, Christos-Savvas Bouganis
The task of compressing pre-trained Deep Neural Networks has attracted wide interest of the research community due to its great benefits in freeing practitioners from data access r…
SAMO: Optimised Mapping of Convolutional Neural Networks to Streaming Architectures
Alexander Montgomerie-Corcoran, Zhewen Yu, Christos-Savvas Bouganis
Significant effort has been placed on the development of toolflows that map Convolutional Neural Network (CNN) models to Field Programmable Gate Arrays (FPGAs) with the aim of auto…