8 papers
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…
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,…
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,…
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…
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.…