SparCE: Sparsity Aware General-Purpose Core Extensions to Accelerate Deep Neural NetworksSanchari SenShubham Jainet al.2019IEEE TC
A Compiler for Deep Neural Network Accelerators to Generate Optimized Code for a Wide Range of Data Parameters from a Hand-crafted Computation KernelEri OgawaKazuaki Ishizakiet al.2019COOL CHIPS 2019
Data Subsetting: A Data-Centric Approach to Approximate ComputingYounghoon KimSwagath Venkataramaniet al.2019DATE 2019
A Scalable Multi-TeraOPS Core for AI Training and InferenceSunil ShuklaBruce Fleischeret al.2018IEEE SSC-L
A Scalable Multi-TeraOPS Deep Learning Processor Core for AI Trainina and InferenceBruce FleischerSunil Shuklaet al.2018VLSI Circuits 2018
DyHard-DNN: Even more DNN acceleration with dynamic hardware reconfigurationMateja PuticAlper Buyuktosunogluet al.2018DAC 2018
Compensated-DNN: Energy efficient low-precision deep neural networks by compensating quantization errorsShubham JainSwagath Venkataramaniet al.2018DAC 2018
Exploiting approximate computing for deep learning accelerationChia-Yu ChenJungwook Choiet al.2018DATE 2018
POSTER: Design Space Exploration for Performance Optimization of Deep Neural Networks on Shared Memory AcceleratorsSwagath VenkataramaniJungwook Choiet al.2017PACT 2017
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System-aware Selective Quantization For Performance Optimized Distributed Deep Learning09 Jan 2023US11551054
MOMori OharaDeputy Director, IBM Research Tokyo, Distinguished Engineer, Chief SW Engineer for Hybrid Cloud on IBM HW