Search Results for author: Clemens JS Schaefer

Found 8 papers, 3 papers with code

Estimating Post-Synaptic Effects for Online Training of Feed-Forward SNNs

1 code implementation7 Nov 2023 Thomas Summe, Clemens JS Schaefer, Siddharth Joshi

We show improved scaling for multi-layer networks using a novel approximation of temporal effects on the subsequent layer's activity.

Hadamard Domain Training with Integers for Class Incremental Quantized Learning

no code implementations5 Oct 2023 Martin Schiemer, Clemens JS Schaefer, Jayden Parker Vap, Mark James Horeni, Yu Emma Wang, Juan Ye, Siddharth Joshi

In this paper, we propose a technique that leverages inexpensive Hadamard transforms to enable low-precision training with only integer matrix multiplications.

class-incremental learning Class Incremental Learning +3

Augmenting Hessians with Inter-Layer Dependencies for Mixed-Precision Post-Training Quantization

no code implementations8 Jun 2023 Clemens JS Schaefer, Navid Lambert-Shirzad, Xiaofan Zhang, Chiachen Chou, Tom Jablin, Jian Li, Elfie Guo, Caitlin Stanton, Siddharth Joshi, Yu Emma Wang

To address this challenge, we propose a mixed-precision post training quantization (PTQ) approach that assigns different numerical precisions to tensors in a network based on their specific needs, for a reduced memory footprint and improved latency while preserving model accuracy.

Quantization

NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems

1 code implementation10 Apr 2023 Jason Yik, Korneel Van den Berghe, Douwe den Blanken, Younes Bouhadjar, Maxime Fabre, Paul Hueber, Weijie Ke, Mina A Khoei, Denis Kleyko, Noah Pacik-Nelson, Alessandro Pierro, Philipp Stratmann, Pao-Sheng Vincent Sun, Guangzhi Tang, Shenqi Wang, Biyan Zhou, Soikat Hasan Ahmed, George Vathakkattil Joseph, Benedetto Leto, Aurora Micheli, Anurag Kumar Mishra, Gregor Lenz, Tao Sun, Zergham Ahmed, Mahmoud Akl, Brian Anderson, Andreas G. Andreou, Chiara Bartolozzi, Arindam Basu, Petrut Bogdan, Sander Bohte, Sonia Buckley, Gert Cauwenberghs, Elisabetta Chicca, Federico Corradi, Guido de Croon, Andreea Danielescu, Anurag Daram, Mike Davies, Yigit Demirag, Jason Eshraghian, Tobias Fischer, Jeremy Forest, Vittorio Fra, Steve Furber, P. Michael Furlong, William Gilpin, Aditya Gilra, Hector A. Gonzalez, Giacomo Indiveri, Siddharth Joshi, Vedant Karia, Lyes Khacef, James C. Knight, Laura Kriener, Rajkumar Kubendran, Dhireesha Kudithipudi, Yao-Hong Liu, Shih-Chii Liu, Haoyuan Ma, Rajit Manohar, Josep Maria Margarit-Taulé, Christian Mayr, Konstantinos Michmizos, Dylan Muir, Emre Neftci, Thomas Nowotny, Fabrizio Ottati, Ayca Ozcelikkale, Priyadarshini Panda, Jongkil Park, Melika Payvand, Christian Pehle, Mihai A. Petrovici, Christoph Posch, Alpha Renner, Yulia Sandamirskaya, Clemens JS Schaefer, André van Schaik, Johannes Schemmel, Samuel Schmidgall, Catherine Schuman, Jae-sun Seo, Sadique Sheik, Sumit Bam Shrestha, Manolis Sifalakis, Amos Sironi, Matthew Stewart, Kenneth Stewart, Terrence C. Stewart, Jonathan Timcheck, Nergis Tömen, Gianvito Urgese, Marian Verhelst, Craig M. Vineyard, Bernhard Vogginger, Amirreza Yousefzadeh, Fatima Tuz Zohora, Charlotte Frenkel, Vijay Janapa Reddi

To address these shortcomings, we present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems.

Benchmarking

The Hardware Impact of Quantization and Pruning for Weights in Spiking Neural Networks

1 code implementation8 Feb 2023 Clemens JS Schaefer, Pooria Taheri, Mark Horeni, Siddharth Joshi

Energy efficient implementations and deployments of Spiking neural networks (SNNs) have been of great interest due to the possibility of developing artificial systems that can achieve the computational powers and energy efficiency of the biological brain.

Gesture Recognition Quantization

Mixed Precision Post Training Quantization of Neural Networks with Sensitivity Guided Search

no code implementations2 Feb 2023 Clemens JS Schaefer, Elfie Guo, Caitlin Stanton, Xiaofan Zhang, Tom Jablin, Navid Lambert-Shirzad, Jian Li, Chiachen Chou, Siddharth Joshi, Yu Emma Wang

In this paper, we propose a method to efficiently determine quantization configurations of different tensors in ML models using post-training mixed precision quantization.

Quantization

Edge Inference with Fully Differentiable Quantized Mixed Precision Neural Networks

no code implementations15 Jun 2022 Clemens JS Schaefer, Siddharth Joshi, Shan Li, Raul Blazquez

Quantizing the parameters and operations to lower bit-precision offers substantial memory and energy savings for neural network inference, facilitating the use of DNNs on edge computing platforms.

Edge-computing Quantization

Memory Organization for Energy-Efficient Learning and Inference in Digital Neuromorphic Accelerators

no code implementations5 Mar 2020 Clemens JS Schaefer, Patrick Faley, Emre O. Neftci, Siddharth Joshi

The energy efficiency of neuromorphic hardware is greatly affected by the energy of storing, accessing, and updating synaptic parameters.

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