7 citations · 11 across the 18 of their papers we have counts for
8 papers · 1 filter
QuArch: A Benchmark for Evaluating LLM Reasoning in Computer Architecture
Shvetank Prakash, Andrew Cheng, Mark Mazumder +24
The field of computer architecture, which bridges high-level software abstractions and low-level hardware implementations, remains absent from current large language model (LLM) ev…
Slm-mux: Orchestrating small language models for reasoning
Chenyu Wang, Zishen Wan, Hao Kang +5
With the rapid development of language models, the number of small language models (SLMs) has grown significantly. Although they do not achieve state-of-the-art accuracy, they are…
Cross-Layer Design of Vector-Symbolic Computing: Bridging Cognition and Brain-Inspired Hardware Acceleration
Shuting Du, Mohamed Ibrahim, Zishen Wan +7
Vector Symbolic Architectures (VSAs) have been widely deployed in various cognitive applications due to their simple and efficient operations. The widespread adoption of VSAs has,…
SCALE-Sim v3: A modular cycle-accurate systolic accelerator simulator for end-to-end system analysis
Ritik Raj, Sarbartha Banerjee, Nikhil Chandra +4
The rapid advancements in AI, scientific computing, and high-performance computing (HPC) have driven the need for versatile and efficient hardware accelerators. Existing tools like…
NSFlow: An End-to-End FPGA Framework with Scalable Dataflow Architecture for Neuro-Symbolic AI
Hanchen Yang, Zishen Wan, Ritik Raj +5
Neuro-Symbolic AI (NSAI) is an emerging paradigm that integrates neural networks with symbolic reasoning to enhance the transparency, reasoning capabilities, and data efficiency of…
HyDra: SOT-CAM Based Vector Symbolic Macro for Hyperdimensional Computing
Md Mizanur Rahaman Nayan, Che-Kai Liu, Zishen Wan +2
Hyperdimensional computing (HDC) is a brain-inspired paradigm valued for its noise robustness, parallelism, energy efficiency, and low computational overhead. Hardware accelerators…