8 citations · 10 across the 10 of their papers we have counts for
6 papers · 1 filter
LLM-based Hardware Development with Hierarchical IRs and End-to-End Multi-Agent Workflow
Chenyang Yin, Agasthi Haputhanthri, Aditya Anirudh Jonnalagadda +7
Large language models (LLMs) are increasingly used in software development, but their use in complex hardware design remains limited. This gap stems from both the scarcity of publi…
A Data-Driven Dynamic Execution Orchestration Architecture
Zhenyu Bai, Pranav Dangi, Rohan Juneja +4
Domain-specific accelerators deliver exceptional performance on their target workloads through fabrication-time orchestrated datapaths. However, such specialized architectures ofte…
TerEffic: Highly Efficient Ternary LLM Inference on FPGA
Chenyang Yin, Zhenyu Bai, Pranav Venkatram +3
Deploying Large Language Models (LLMs) efficiently on edge devices is often constrained by limited memory capacity and high power consumption. Low-bit quantization methods, particu…
Enhancing CGRA Efficiency Through Aligned Compute and Communication Provisioning
Zhaoying Li, Pranav Dangi, Chenyang Yin +5
Coarse-grained Reconfigurable Arrays (CGRAs) are domain-agnostic accelerators that enhance the energy efficiency of resource-constrained edge devices. The CGRA landscape is diverse…
SparrowSNN: A Hardware/software Co-design for Energy Efficient ECG Classification
Zhanglu Yan, Zhenyu Bai, Tulika Mitra +1
Deep learning has driven significant technological advancements, but its high energy consumption limits its use on battery-operated edge devices. Spiking Neural Networks (SNNs) off…
SWAT: Scalable and Efficient Window Attention-based Transformers Acceleration on FPGAs
Zhenyu Bai, Pranav Dangi, Huize Li +1
Efficiently supporting long context length is crucial for Transformer models. The quadratic complexity of the self-attention computation plagues traditional Transformers. Sliding w…