| In-memory low-cost bit-serial addition using commodity DRAM technology MF Ali, A Jaiswal, K Roy IEEE Transactions on Circuits and Systems I: Regular Papers 67 (1), 155-165, 2019 | 112 | 2019 |
| IMAC: In-memory multi-bit multiplication and accumulation in 6T SRAM array M Ali, A Jaiswal, S Kodge, A Agrawal, I Chakraborty, K Roy IEEE Transactions on Circuits and Systems I: Regular Papers 67 (8), 2521-2531, 2020 | 111 | 2020 |
| GENIEx: A Generalized Approach to Emulating Non-Ideality in Memristive Xbars using Neural Networks I Chakraborty, MF Ali, DE Kim, A Ankit, K Roy 2020 57th ACM/IEEE Design Automation Conference (DAC), 6, 2020 | 90 | 2020 |
| Resistive crossbars as approximate hardware building blocks for machine learning: Opportunities and challenges I Chakraborty, M Ali, A Ankit, S Jain, S Roy, S Sridharan, A Agrawal, ... Proceedings of the IEEE 108 (12), 2276-2310, 2020 | 87 | 2020 |
| In-memory computing in emerging memory technologies for machine learning: An overview K Roy, I Chakraborty, M Ali, A Ankit, A Agrawal 2020 57th ACM/IEEE Design Automation Conference (DAC), 1-6, 2020 | 65 | 2020 |
| Circuits and architectures for in-memory computing-based machine learning accelerators A Ankit, I Chakraborty, A Agrawal, M Ali, K Roy IEEE Micro 40 (6), 8-22, 2020 | 34 | 2020 |
| IMPULSE: A 65nm Digital Compute-in-Memory Macro with Fused Weights and Membrane Potential for Spike-based Sequential Learning Tasks A Agrawal, M Ali, M Koo, N Rathi, A Jaiswal, K Roy IEEE Solid-State Circuits Letters 4, 137-140, 2021 | 29 | 2021 |
| i-SRAM: Interleaved Wordlines for Vector Boolean Operations Using SRAMs A Jaiswal, A Agrawal, MF Ali, S Sharmin, K Roy IEEE Transactions on Circuits and Systems I: Regular Papers 67 (12), 4651-4659, 2020 | 26 | 2020 |
| A 35.5-127.2 tops/w dynamic sparsity-aware reconfigurable-precision compute-in-memory sram macro for machine learning M Ali, I Chakraborty, U Saxena, A Agrawal, A Ankit, K Roy IEEE Solid-State Circuits Letters 4, 129-132, 2021 | 23 | 2021 |
| RAMANN: in-SRAM differentiable memory computations for memory-augmented neural networks M Ali, A Agrawal, K Roy Proceedings of the ACM/IEEE International Symposium on Low Power Electronics …, 2020 | 22 | 2020 |
| OTFTs compact models: analysis, comparison, and insights M Fayez, KM Morsi, MN Sabry IET Circuits, Devices & Systems 11 (5), 409-420, 2017 | 21 | 2017 |
| Low-Precision Training in Logarithmic Number System using Multiplicative Weight Update J Zhao, S Dai, R Venkatesan, B Zimmer, M Ali, MY Liu, B Khailany, ... Thirty-fifth Conference on Neural Information Processing Systems, NeurIPS …, 2021 | 17* | 2021 |
| PIM-DRAM: Accelerating machine learning workloads using processing in commodity DRAM S Roy, M Ali, A Raghunathan IEEE Journal on Emerging and Selected Topics in Circuits and Systems 11 (4 …, 2021 | 16 | 2021 |
| Compute-in-memory technologies and architectures for deep learning workloads M Ali, S Roy, U Saxena, T Sharma, A Raghunathan, K Roy IEEE Transactions on Very Large Scale Integration (VLSI) Systems 30 (11 …, 2022 | 15 | 2022 |
| Dynamic Read Current Sensing with Amplified Bit-line Voltage for STT-MRAMs MF Ali, R Andrawis, K Roy IEEE Transactions on Circuits and Systems II: Express Briefs 99 (99), 1-5, 2019 | 10 | 2019 |
| A 65 nm 1.4-6.7 TOPS/W adaptive-SNR sparsity-aware CIM core with load balancing support for DL workloads M Ali, I Chakraborty, S Choudhary, M Chang, DE Kim, A Raychowdhury, ... 2023 IEEE Custom Integrated Circuits Conference (CICC), 1-2, 2023 | 7 | 2023 |
| Sourjya Roy, Shrihari Sridharan, Amogh Agrawal, Anand Raghunathan, and Kaushik Roy. Resistive crossbars as approximate hardware building blocks for machine learning … I Chakraborty, M Ali, A Ankit, S Jain Proceedings of the IEEE 108 (12), 2276-2310, 2020 | 6 | 2020 |
| All-ink fabrication of a TIPS-pentacene OTFT using plotter printing technique in all-air environment M Fayez, K Morsi, MN Sabry 2017 Intl Conf on Advanced Control Circuits Systems (ACCS) Systems & 2017 …, 2017 | 4 | 2017 |
| Simulation of organic thin film transistor at both device and circuit levels M Fayez, K Morsy, MN Sabry International Conference on Aerospace Sciences and Aviation Technology 16 …, 2015 | 4 | 2015 |
| Sourjya Roy, S. Sridharan, Amogh Agrawal, A. Raghunathan, and K. Roy. Resistive crossbars as approximate hardware building blocks for machine learning: Opportunities and challenges I Chakraborty, M Ali, A Ankit, S Jain Proceedings of the IEEE 108, 2276-2310, 2020 | 3 | 2020 |
Kaushik RoyProfessor of Electrical and Computer Engineering, Purdue UniversityVerified email at purdue.edu
