40 citations · 51 across the 9 of their papers we have counts for
9 papers
Less is More: Hop-Wise Graph Attention for Scalable and Generalizable Learning on Circuits
Chenhui Deng, Zichao Yue, Cunxi Yu +4
While graph neural networks (GNNs) have gained popularity for learning circuit representations in various electronic design automation (EDA) tasks, they face challenges in scalabil…
Radial Networks: Dynamic Layer Routing for High-Performance Large Language Models
Jordan Dotzel, Yash Akhauri, Ahmed S. AbouElhamayed +3
Large language models (LLMs) often struggle with strict memory, latency, and power demands. To meet these demands, various forms of dynamic sparsity have been proposed that reduce…
Allo: A Programming Model for Composable Accelerator Design
Hongzheng Chen, Niansong Zhang, Shaojie Xiang +3
Special-purpose hardware accelerators are increasingly pivotal for sustaining performance improvements in emerging applications, especially as the benefits of technology scaling co…
UniSparse: An Intermediate Language for General Sparse Format Customization
Jie Liu, Zhongyuan Zhao, Zijian Ding +3
The ongoing trend of hardware specialization has led to a growing use of custom data formats when processing sparse workloads, which are typically memory-bound. These formats facil…
Exploring the Limits of Semantic Image Compression at Micro-bits per Pixel
Jordan Dotzel, Bahaa Kotb, James Dotzel +2
Traditional methods, such as JPEG, perform image compression by operating on structural information, such as pixel values or frequency content. These methods are effective to bitra…
Trainable Fixed-Point Quantization for Deep Learning Acceleration on FPGAs
Dingyi Dai, Yichi Zhang, Jiahao Zhang +4
Quantization is a crucial technique for deploying deep learning models on resource-constrained devices, such as embedded FPGAs. Prior efforts mostly focus on quantizing matrix mult…