108 citations · 234 across the 37 of their papers we have counts for
41 papers
AgenticSizing: A Large Language Model-based Multi-Agent Framework for Analog Circuit Sizing
Yijia Hao, Pratibha Verma, Dongxu Guo +4
Analog circuit sizing remains a challenging and time-consuming task due to the large design space, strong performance trade-offs, and increasing circuit complexity in scaled techno…
A flexible language model-assisted electronic design automation framework
Cristian Sestito, Panagiota Kontou, Pratibha Verma +5
Large language models (LLMs) are transforming electronic design automation (EDA) by enhancing design stages such as schematic design, simulation, netlist synthesis, and place-and-r…
GatedFWA: Linear Flash Windowed Attention with Gated Associative Memory
Jiaxu Liu, Yuhe Bai, Xiangyu Yin +1
Modern autoregressive models rely on attention, yet the Softmax full attention in Transformers scales quadratically with sequence length. Sliding Window Attention (SWA) achieves li…
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…
Cached Multi-Lora Composition for Multi-Concept Image Generation
Xiandong Zou, Mingzhu Shen, Christos-Savvas Bouganis +1
Low-Rank Adaptation (LoRA) has emerged as a widely adopted technique in text-to-image models, enabling precise rendering of multiple distinct elements, such as characters and style…
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…
-DiT: A Training-Free Acceleration Method Tailored for Diffusion Transformers
Pengtao Chen, Mingzhu Shen, Peng Ye +5
Diffusion models are widely recognized for generating high-quality and diverse images, but their poor real-time performance has led to numerous acceleration works, primarily focusi…
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…
Towards Understanding Why Label Smoothing Degrades Selective Classification and How to Fix It
Guoxuan Xia, Olivier Laurent, Gianni Franchi +1
Label smoothing (LS) is a popular regularisation method for training neural networks as it is effective in improving test accuracy and is simple to implement. ``Hard'' one-hot labe…
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…
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…
FMM-X3D: FPGA-based modeling and mapping of X3D for Human Action Recognition
Petros Toupas, Christos-Savvas Bouganis, Dimitrios Tzovaras
3D Convolutional Neural Networks are gaining increasing attention from researchers and practitioners and have found applications in many domains, such as surveillance systems, auto…
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…
Window-Based Early-Exit Cascades for Uncertainty Estimation: When Deep Ensembles are More Efficient than Single Models
Guoxuan Xia, Christos-Savvas Bouganis
Deep Ensembles are a simple, reliable, and effective method of improving both the predictive performance and uncertainty estimates of deep learning approaches. However, they are wi…
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…
On the Usefulness of Deep Ensemble Diversity for Out-of-Distribution Detection
Guoxuan Xia, Christos-Savvas Bouganis
The ability to detect Out-of-Distribution (OOD) data is important in safety-critical applications of deep learning. The aim is to separate In-Distribution (ID) data drawn from the…
Augmenting Softmax Information for Selective Classification with Out-of-Distribution Data
Guoxuan Xia, Christos-Savvas Bouganis
Detecting out-of-distribution (OOD) data is a task that is receiving an increasing amount of research attention in the domain of deep learning for computer vision. However, the per…
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).…
Low-Cost On-device Partial Domain Adaptation (LoCO-PDA): Enabling efficient CNN retraining on edge devices
Aditya Rajagopal, Christos-Savvas Bouganis
With the increased deployment of Convolutional Neural Networks (CNNs) on edge devices, the uncertainty of the observed data distribution upon deployment has led researchers to to u…