| A 0.086-mm 12.7-pJ/SOP 64k-Synapse 256-Neuron Online-Learning Digital Spiking Neuromorphic Processor in 28nm CMOS C Frenkel, M Lefebvre, JD Legat, D Bol IEEE Transactions on Biomedical Circuits and Systems 13 (1), 145-158, 2019 | 355 | 2019 |
| Hand-gesture recognition based on EMG and event-based camera sensor fusion: A benchmark in neuromorphic computing E Ceolini*, C Frenkel*, SB Shrestha*, G Taverni, L Khacef, M Payvand, ... Frontiers in Neuroscience 14, 2020 | 157 | 2020 |
| MorphIC: A 65-nm 738k-Synapse/mm Quad-Core Binary-Weight Digital Neuromorphic Processor with Stochastic Spike-Driven Online Learning C Frenkel, JD Legat, D Bol IEEE Transactions on Biomedical Circuits and Systems 13 (5), 999-1010, 2019 | 138 | 2019 |
| Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks C Frenkel*, M Lefebvre*, D Bol Frontiers in neuroscience 15, 2021 | 94* | 2021 |
| SleepTalker: A ULV 802.15. 4a IR-UWB transmitter SoC in 28-nm FDSOI achieving 14 pJ/b at 27 Mb/s with channel selection based on adaptive FBB and digitally programmable pulse … G de Streel, F Stas, T Gurne, F Durant, C Frenkel, A Cathelin, D Bol IEEE Journal of Solid-State Circuits 52 (4), 1163-1177, 2017 | 71 | 2017 |
| Bottom-up and top-down approaches for the design of neuromorphic processing systems: tradeoffs and synergies between natural and artificial intelligence C Frenkel, D Bol, G Indiveri Proceedings of the IEEE 111 (6), 623-652, 2023 | 68* | 2023 |
| ReckOn: A 28nm sub-mm2 task-agnostic spiking recurrent neural network processor enabling on-chip learning over second-long timescales C Frenkel, G Indiveri 2022 IEEE International Solid-State Circuits Conference (ISSCC) 65, 1-3, 2022 | 59 | 2022 |
| Spiking neural network integrated circuits: A review of trends and future directions A Basu*, L Deng*, C Frenkel*, X Zhang* 2022 IEEE Custom Integrated Circuits Conference (CICC), 1-8, 2022 | 53 | 2022 |
| A 28-nm Convolutional Neuromorphic Processor Enabling Online Learning with Spike-Based Retinas C Frenkel, JD Legat, D Bol 2020 IEEE International Symposium on Circuits and Systems (ISCAS), 1-5, 2020 | 43 | 2020 |
| 19.6 A 40-to-80MHz Sub-4μw/mhz ULV cortex-M0 MCU SoC in 28nm FDSOI with dual-loop adaptive back-bias generator for 20μs wake-up from deep fully retentive sleep mode D Bol, M Schramme, L Moreau, T Haine, P Xu, C Frenkel, R Dekimpe, ... 2019 IEEE International Solid-State Circuits Conference-(ISSCC), 322-324, 2019 | 34 | 2019 |
| PCM-trace: Scalable Synaptic Eligibility Traces with Resistivity Drift of Phase-Change Materials Y Demirağ, F Moro, T Dalgaty, G Navarro, C Frenkel, G Indiveri, ... 2021 IEEE International Symposium on Circuits and Systems (ISCAS), 1-5, 2021 | 29 | 2021 |
| SleepRunner: A 28-nm FDSOI ULP Cortex-M0 MCU With ULL SRAM and UFBR PVT Compensation for 2.6–3.6-μW/DMIPS 40–80-MHz Active Mode and 131-nW … D Bol, M Schramme, L Moreau, P Xu, R Dekimpe, R Saeidi, T Haine, ... IEEE Journal of Solid-State Circuits 56 (7), 2256-2269, 2021 | 28 | 2021 |
| A fully-synthesized 20-gate digital spike-based synapse with embedded online learning C Frenkel, G Indiveri, JD Legat, D Bol 2017 IEEE international symposium on circuits and systems (ISCAS), 1-4, 2017 | 22 | 2017 |
| Online training of spiking recurrent neural networks with phase-change memory synapses Y Demirag, C Frenkel, M Payvand, G Indiveri arXiv preprint arXiv:2108.01804, 2021 | 20 | 2021 |
| A compact phenomenological digital neuron implementing the 20 Izhikevich behaviors C Frenkel, JD Legat, D Bol 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS), 1-4, 2017 | 17 | 2017 |
| Sparsity provides a competitive advantage C Frenkel Nature Machine Intelligence 3 (9), 742-743, 2021 | 15 | 2021 |
| Neurobench: Advancing neuromorphic computing through collaborative, fair and representative benchmarking J Yik, SH Ahmed, Z Ahmed, B Anderson, AG Andreou, C Bartolozzi, ... Jason Yik, Soikat Hasan Ahmed, Zergham Ahmed, Brian Anderson, Andreas G …, 2023 | 14 | 2023 |
| Stochastic dendrites enable online learning in mixed-signal neuromorphic processing systems M Cartiglia, A Rubino, S Narayanan, C Frenkel, G Haessig, G Indiveri, ... 2022 IEEE International Symposium on Circuits and Systems (ISCAS), 476-480, 2022 | 11 | 2022 |
| Bottom-up and top-down neuromorphic processor design: Unveiling roads to embedded cognition C Frenkel UCLouvain Institute for Information and Communication Technologies …, 2020 | 11 | 2020 |
| A 65-nm 738k-Synapse/mm2 Quad-Core Binary-Weight Digital Neuromorphic Processor with Stochastic Spike-Driven Online Learning C Frenkel, JD Legat, D Bol 2019 IEEE International Symposium on Circuits and Systems (ISCAS), 1-5, 2019 | 11 | 2019 |
David BolECS group, ICTEAM institute, Université catholique de Louvain在 uclouvain.be 的电子邮件经过验证
