Quantizing Spiking Neural Networks with Integers. - Clemens J. S. Schaefer; Siddharth Joshi 001 (ICONS, 2020)

Memory Organization for Energy-Efficient Learning and Inference in Digital Neuromorphic Accelerators. - Clemens J. S. Schaefer; Patrick Faley; Emre O. Neftci; Siddharth Joshi 001 (ISCAS, 2020)

Analog vs. Digital Spatial Transforms: A Throughput, Power, and Area Comparison. - Zephan M. Enciso; Seyed Hadi Mirfarshbafan; Oscar Castañeda; Clemens J. S. Schaefer; Christoph Studer; Siddharth Joshi 001 (MWSCAS, 2020)

Memory Organization and Structures for On-Chip Learning in Spiking Neural Networks. - Clemens J. S. Schaefer; Siddharth Joshi 001 (MWSCAS, 2020)

LSTMs for Keyword Spotting with ReRAM-Based Compute-In-Memory Architectures. - Clemens J. S. Schaefer; Mark Horeni; Pooria Taheri; Siddharth Joshi 001 (ISCAS, 2021)

Edge AI without Compromise: Efficient, Versatile and Accurate Neurocomputing in Resistive Random-Access Memory. - Weier Wan; Rajkumar Kubendran; Clemens J. S. Schaefer; Sukru Burc Eryilmaz; Wenqiang Zhang; Dabin Wu; Stephen R. Deiss; Priyanka Raina; He Qian; Bin Gao 006; Siddharth Joshi 001; Huaqiang Wu; et al. (CoRR, 2021)

A compute-in-memory chip based on resistive random-access memory. - Weier Wan; Rajkumar Kubendran; Clemens J. S. Schaefer; Sukru Burc Eryilmaz; Wenqiang Zhang; Dabin Wu; Stephen R. Deiss; Priyanka Raina; He Qian; Bin Gao 006; Siddharth Joshi 001; Huaqiang Wu; et al. (Nat., 2022)

Mixed Precision Post Training Quantization of Neural Networks with Sensitivity Guided Search. - Clemens JS Schaefer; Elfie Guo; Caitlin Stanton; Xiaofan Zhang 001; Tom Jablin; Navid Lambert-Shirzad; Jian Li; Chiachen Chou; Siddharth Joshi 001; Yu Emma Wang (CoRR, 2023)

NeuroBench: Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking. - Jason Yik; Soikat Hasan Ahmed; Zergham Ahmed; Brian Anderson; Andreas G. Andreou; Chiara Bartolozzi; Arindam Basu; Douwe den Blanken; Petrut Bogdan; Sander M. Bohté; Younes Bouhadjar; Sonia M. Buckley; et al. (CoRR, 2023)

Augmenting Hessians with Inter-Layer Dependencies for Mixed-Precision Post-Training Quantization. - Clemens JS Schaefer; Navid Lambert-Shirzad; Xiaofan Zhang 001; Chiachen Chou; Tom Jablin; Jian Li; Elfie Guo; Caitlin Stanton; Siddharth Joshi 001; Yu Emma Wang (CoRR, 2023)

Hadamard Domain Training with Integers for Class Incremental Quantized Learning. - Martin Schiemer; Clemens JS Schaefer; Jayden Parker Vap; Mark James Horeni; Yu Emma Wang; Juan Ye; Siddharth Joshi 001 (CoRR, 2023)

The Hardware Impact of Quantization and Pruning for Weights in Spiking Neural Networks. - Clemens JS Schaefer; Pooria Taheri; Mark Horeni; Siddharth Joshi 001 (IEEE Trans. Circuits Syst. II Express Briefs, 2023)

Hadamard Domain Training with Integers for Class Incremental Quantized Learning. - Martin Schiemer; Clemens JS Schaefer; Mark James Horeni; Yu Emma Wang; Juan Ye; Siddharth Joshi 001 (CoLLAs, 2024)

Estimating Post-Synaptic Effects for Online Training of Feed-Forward SNNs. - Thomas M. Summe; Clemens JS Schaefer; Siddharth Joshi 001 (ICONS, 2024)

Edge Inference with Fully Differentiable Quantized Mixed Precision Neural Networks. - Clemens J. S. Schaefer; Siddharth Joshi 001; Shan Li; Raúl Blázquez (WACV, 2024)

GuidedQuant: Large Language Model Quantization via Exploiting End Loss Guidance. - Jinuk Kim; Marwa El Halabi; Wonpyo Park; Clemens JS Schaefer; Deokjae Lee; Yeonhong Park; Jae W. Lee; Hyun Oh Song (CoRR, 2025)