11 citations · 15 across the 5 of their papers we have counts for
5 papers
A VM-HDL Co-Simulation Framework for Systems with PCIe-Connected FPGAs
Shenghsun Cho, Mrunal Patel, Basavaraj Kaladagi +4
PCIe-connected FPGAs are gaining popularity as an accelerator technology in data centers. However, it is challenging to jointly develop and debug host software and FPGA hardware. C…
Efficient Methods for Natural Language Processing: A Survey
Marcos Treviso, Ji-Ung Lee, Tianchu Ji +19
Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance mea…
On the Distribution, Sparsity, and Inference-time Quantization of Attention Values in Transformers
Tianchu Ji, Shraddhan Jain, Michael Ferdman +3
How much information do NLP tasks really need from a transformer's attention mechanism at application-time (inference)? From recent work, we know that there is sparsity in transfor…
Medusa: A Scalable Interconnect for Many-Port DNN Accelerators and Wide DRAM Controller Interfaces
Yongming Shen, Tianchu Ji, Michael Ferdman +1
To cope with the increasing demand and computational intensity of deep neural networks (DNNs), industry and academia have turned to accelerator technologies. In particular, FPGAs h…
Maximizing CNN Accelerator Efficiency Through Resource Partitioning
Yongming Shen, Michael Ferdman, Peter Milder
Convolutional neural networks (CNNs) are revolutionizing machine learning, but they present significant computational challenges. Recently, many FPGA-based accelerators have been p…