Archit Gajjar — Papers on Cortexa

arxivcs.ARcs.AIcs.ET2026-07-24

Multi-primitive in-memory computing for Monte Carlo tree search

Tergel Molom-Ochir, Benjamin F. Morris, Yintao He, Archit Gajjar, Giacomo Pedretti, Hai Helen Li, et al.

Monte Carlo tree search (MCTS) enables artificial intelligence (AI) decision-making, but requires 55-300 W on conventional processors, limiting edge deployment. In-memory computing (IMC) is energy-efficient on regular workloads but has been considered incompatible with irregular…

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arxivcs.ARcs.AI2026-07-16

NIFA: Nonlinear IMC enhanced FPGA for efficient ML inference

Jiajun Hu, Ruthwik Reddy Sunketa, Lei Zhao, Archit Gajjar, Luca Buonanno, Aman Arora

Recent FPGAs have improved deep learning (DL) inference efficiency through dedicated tensor blocks and in-BRAM computation. ReRAM-based analog in-memory computing (IMC) pushes efficiency further, offering an order-of-magnitude improvement in compute density and energy efficiency…

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