13 papers
ORACLE: Agentic AI Orchestrator Routing Via Adaptive Verifier Calibration Feedback
Ritik Raj, Souvik Kundu, Sarbartha Banerjee +3
Modern enterprise agent deployments consist of a heterogeneous pool of large language models (LLMs) having diverse capabilities and cost. Existing model routing strategies optimize…
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…
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…
Towards Understanding, Analyzing, and Optimizing Agentic AI Execution: A CPU-Centric Perspective
Ritik Raj, Souvik Kundu, Ishita Vohra +2
Agentic AI serving converts monolithic LLM-based inference to autonomous problem-solvers that can plan, call tools, perform reasoning, and adapt on the fly. Due to diverse task exe…
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…
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…
MCMComm: Hardware-Software Co-Optimization for End-to-End Communication in Multi-Chip-Modules
Ritik Raj, Shengjie Lin, William Won +1
Increasing AI computing demands and slowing transistor scaling have led to the advent of Multi-Chip-Module (MCMs) based accelerators. MCMs enable cost-effective scalability, higher…
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…
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.…
Axon: A novel systolic array architecture for improved run time and energy efficient GeMM and Conv operation with on-chip im2col
Md Mizanur Rahaman Nayan, Ritik Raj, Gouse Basha Shaik +2
General matrix multiplication (GeMM) is a core operation in virtually all AI applications. Systolic array (SA) based architectures have shown great promise as GeMM hardware acceler…
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…