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qCG: A Low-Power Multi-Domain SFQ Logic Design and Verification Framework., , and . ICCD, page 446-449. IEEE, (2019)OCTAN: An On-Chip Training Algorithm for Memristive Neuromorphic Circuits., , , , and . IEEE Trans. Circuits Syst. I Regul. Pap., 66-I (12): 4687-4698 (2019)Sparse Periodic Systolic Dataflow for Lowering Latency and Power Dissipation of Convolutional Neural Network Accelerators., , , and . ISLPED, page 3:1-3:6. ACM, (2022)SEERAD: A high speed yet energy-efficient rounding-based approximate divider., , , , , and . DATE, page 1481-1484. IEEE, (2016)NullaNet Tiny: Ultra-low-latency DNN Inference Through Fixed-function Combinational Logic., , , , , and . FCCM, page 266-267. IEEE, (2021)A Hybrid Framework for Functional Verification using Reinforcement Learning and Deep Learning., , , , , and . ACM Great Lakes Symposium on VLSI, page 367-370. ACM, (2019)CSrram: Area-Efficient Low-Power Ex-Situ Training Framework for Memristive Neuromorphic Circuits Based on Clustered Sparsity., , , , and . ISVLSI, page 465-470. IEEE, (2019)VeriSFQ: A Semi-formal Verification Framework and Benchmark for Single Flux Quantum Technology., , , , , and . ISQED, page 224-230. IEEE, (2019)PHAX: Physical Characteristics Aware Ex-Situ Training Framework for Inverter-Based Memristive Neuromorphic Circuits., , , , , , and . IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 37 (8): 1602-1613 (2018)Have your QEC and Bandwidth too!: A lightweight cryogenic decoder for common / trivial errors, and efficient bandwidth + execution management otherwise., , , , , , and . CoRR, (2022)