Research

I design memristive associative memory hardware for AI inference and reasoning, working across the device–circuit–architecture stack to make associative computation a first-class primitive in modern AI systems. My work spans three thrusts:

Memristive Substrates

Device-level cells, volumetric arrays, and characterization for in-memory computing.

Projects: 3D Cellular Neural Networks for volumetric computing (with Chua, Williams, Xia); RRAM crossbar device characterization with HPE Labs.

Associative Memory Circuits

CAM cells, arrays, and architectures for attention, retrieval, and similarity matching at hardware scale.

Projects: CAMformer (associative memory for attention), MonoSparse-CAM (tree models in CAM), NP-CAM (scalable CAM architectures), and a comprehensive review of 37 CAM cell designs in IEEE TCAS-I.

Hardware for Inference and Reasoning

Algorithm–hardware co-design for the workloads associative memory enables — attention, tree search, neuro-symbolic reasoning, and test-time compute.

Projects: IMC-MCTS (in-memory tree search), Neuro-Symbolic Policy (rule lookup in CAM), Hamming Attention Distillation (binarized attention).