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A 28nm 1.80Mb/mm2 Digital/Analog Hybrid SRAM-CIM Macro Using 2D-Weighted Capacitor Array for Complex Number Mac Operations
Shota Konno, Che-Kai Liu, Sigang Ryu +2
A 28nm dense 6T-SRAM Digital(D)/Analog(A) Hybrid compute-in-memory (CIM) macro supporting complex num-ber MAC operation is presented. By introducing a 2D-weighted Capacitor Array,…
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,…
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…
MIST: A Co-Design Framework for Heterogeneous, Multi-Stage LLM Inference
Abhimanyu Rajeshkumar Bambhaniya, Hanjiang Wu, Suvinay Subramanian +8
Modern LLM serving now spans multi-stage pipelines including RAG retrieval and KV cache reuse, each with distinct compute, memory, and latency demands. Inference engines expose a l…
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.…
Characterization and Mitigation of ADC Noise by Reference Tuning in RRAM-Based Compute-In-Memory
Ying-Hao Wei, Zishen Wan, Brian Crafton +2
With the escalating demand for power-efficient neural network architectures, non-volatile compute-in-memory designs have garnered significant attention. However, owing to the natur…