108 citations · 234 across the 37 of their papers we have counts for
12 papers · 1 filter
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…
fpgaHART: A toolflow for throughput-oriented acceleration of 3D CNNs for HAR onto FPGAs
Petros Toupas, Christos-Savvas Bouganis, Dimitrios Tzovaras
Surveillance systems, autonomous vehicles, human monitoring systems, and video retrieval are just few of the many applications in which 3D Convolutional Neural Networks are exploit…
ATHEENA: A Toolflow for Hardware Early-Exit Network Automation
Benjamin Biggs, Christos-Savvas Bouganis, George A. Constantinides
The continued need for improvements in accuracy, throughput, and efficiency of Deep Neural Networks has resulted in a multitude of methods that make the most of custom architecture…
HARFLOW3D: A Latency-Oriented 3D-CNN Accelerator Toolflow for HAR on FPGA Devices
Petros Toupas, Alexander Montgomerie-Corcoran, Christos-Savvas Bouganis +1
For Human Action Recognition tasks (HAR), 3D Convolutional Neural Networks have proven to be highly effective, achieving state-of-the-art results. This study introduces a novel str…
Multi-DNN Accelerators for Next-Generation AI Systems
Stylianos I. Venieris, Christos-Savvas Bouganis, Nicholas D. Lane
As the use of AI-powered applications widens across multiple domains, so do increase the computational demands. Primary driver of AI technology are the deep neural networks (DNNs).…
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…