11 citations · 24 across the 9 of their papers we have counts for
9 papers
Estimating Post-Synaptic Effects for Online Training of Feed-Forward SNNs
Thomas Summe, Clemens JS Schaefer, Siddharth Joshi
Facilitating online learning in spiking neural networks (SNNs) is a key step in developing event-based models that can adapt to changing environments and learn from continuous data…
Hadamard Domain Training with Integers for Class Incremental Quantized Learning
Martin Schiemer, Clemens JS Schaefer, Jayden Parker Vap +4
Continual learning is a desirable feature in many modern machine learning applications, which allows in-field adaptation and updating, ranging from accommodating distribution shift…
Augmenting Hessians with Inter-Layer Dependencies for Mixed-Precision Post-Training Quantization
Clemens JS Schaefer, Navid Lambert-Shirzad, Xiaofan Zhang +7
Efficiently serving neural network models with low latency is becoming more challenging due to increasing model complexity and parameter count. Model quantization offers a solution…
The Hardware Impact of Quantization and Pruning for Weights in Spiking Neural Networks
Clemens JS Schaefer, Pooria Taheri, Mark Horeni +1
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 achi…
Mixed Precision Post Training Quantization of Neural Networks with Sensitivity Guided Search
Clemens JS Schaefer, Elfie Guo, Caitlin Stanton +7
Serving large-scale machine learning (ML) models efficiently and with low latency has become challenging owing to increasing model size and complexity. Quantizing models can simult…
Edge Inference with Fully Differentiable Quantized Mixed Precision Neural Networks
Clemens JS Schaefer, Siddharth Joshi, Shan Li +1
The large computing and memory cost of deep neural networks (DNNs) often precludes their use in resource-constrained devices. Quantizing the parameters and operations to lower bit-…
Edge AI without Compromise: Efficient, Versatile and Accurate Neurocomputing in Resistive Random-Access Memory
Weier Wan, Rajkumar Kubendran, Clemens Schaefer +11
Realizing today's cloud-level artificial intelligence functionalities directly on devices distributed at the edge of the internet calls for edge hardware capable of processing mult…
Analog vs. Digital Spatial Transforms: A Throughput, Power, and Area Comparison
Zephan M. Enciso, Seyed Hadi Mirfarshbafan, Oscar Castañeda +3
Spatial linear transforms that process multiple parallel analog signals to simplify downstream signal processing find widespread use in multi-antenna communication systems, machine…
Memory Organization for Energy-Efficient Learning and Inference in Digital Neuromorphic Accelerators
Clemens JS Schaefer, Patrick Faley, Emre O Neftci +1
The energy efficiency of neuromorphic hardware is greatly affected by the energy of storing, accessing, and updating synaptic parameters. Various methods of memory organisation tar…