7 citations · 11 across the 18 of their papers we have counts for
19 papers
ArchEval: Measuring AI Agents as Computer Architects
Chenyu Wang, Zishen Wan, Jeffrey Ma +8
Computer architecture has long used benchmarks to make progress measurable. LLM agents create a different measurement problem: success is not merely writing code or tuning paramete…
Serving Agentic Workflows with a Physical-Plan Compiler and Adaptive Runtime
Jiayi Qian, Yichong Zhang, Zishen Wan +4
Efficient serving of agentic workflows requires selecting each LLM node's model, verification policy, and backend to balance output quality, latency, and throughput. These assignme…
AgentDSE: Reasoning-Augmented Architectural Design Space Exploration
Chenyu Wang, Jiahe Caroline Shi, David Kong +4
Traditional architectural design space exploration (DSE) is highly inefficient, typically requiring tens of thousands of simulator evaluations across various optimization methods.…
FLARE: One-Shot PE-Level Fault Localization in Systolic Arrays via Algebraic Test Vectors
Logashree Venkatasubramanian, Zishen Wan, Viveck Cadambe
Systolic arrays are the dominant compute fabric for neural network inference. Prior work has addressed column-level fault detection efficiently with uniform test patterns, but row-…
REASON: Accelerating Probabilistic Logical Reasoning for Scalable Neuro-Symbolic Intelligence
Zishen Wan, Che-Kai Liu, Jiayi Qian +3
Neuro-symbolic AI systems integrate neural perception with symbolic reasoning to enable data-efficient, interpretable, and robust intelligence beyond purely neural models. Although…
SATA: Sparsity-Aware Scheduling for Selective Token Attention
Zhenkun Fan, Zishen Wan, Che-Kai Liu +5
Transformers have become the foundation of numerous state-of-the-art AI models across diverse domains, thanks to their powerful attention mechanism for modeling long-range dependen…
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…
Generative AI in Embodied Systems: System-Level Analysis of Performance, Efficiency and Scalability
Zishen Wan, Jiayi Qian, Yuhang Du +6
Embodied systems, where generative autonomous agents engage with the physical world through integrated perception, cognition, action, and advanced reasoning powered by large langua…
CogSys: Efficient and Scalable Neurosymbolic Cognition System via Algorithm-Hardware Co-Design
Zishen Wan, Hanchen Yang, Ritik Raj +4
Neurosymbolic AI is an emerging compositional paradigm that fuses neural learning with symbolic reasoning to enhance the transparency, interpretability, and trustworthiness of AI.…
Towards Efficient Neuro-Symbolic AI: From Workload Characterization to Hardware Architecture
Zishen Wan, Che-Kai Liu, Hanchen Yang +13
The remarkable advancements in artificial intelligence (AI), primarily driven by deep neural networks, are facing challenges surrounding unsustainable computational trajectories, l…
H3DFact: Heterogeneous 3D Integrated CIM for Factorization with Holographic Perceptual Representations
Zishen Wan, Che-Kai Liu, Mohamed Ibrahim +4
Disentangling attributes of various sensory signals is central to human-like perception and reasoning and a critical task for higher-order cognitive and neuro-symbolic AI systems.…
Algorithm-Hardware Co-Design of Distribution-Aware Logarithmic-Posit Encodings for Efficient DNN Inference
Akshat Ramachandran, Zishen Wan, Geonhwa Jeong +2
Traditional Deep Neural Network (DNN) quantization methods using integer, fixed-point, or floating-point data types struggle to capture diverse DNN parameter distributions at low p…
Towards Cognitive AI Systems: a Survey and Prospective on Neuro-Symbolic AI
Zishen Wan, Che-Kai Liu, Hanchen Yang +7
The remarkable advancements in artificial intelligence (AI), primarily driven by deep neural networks, have significantly impacted various aspects of our lives. However, the curren…
SEE-MCAM: Scalable Multi-bit FeFET Content Addressable Memories for Energy Efficient Associative Search
Shengxi Shou, Che-Kai Liu, Sanggeon Yun +7
In this work, we propose SEE-MCAM, scalable and compact multi-bit CAM (MCAM) designs that utilize the three-terminal ferroelectric FET (FeFET) as the proxy. By exploiting the multi…