Search Results for author:
Found 8 papers, 3 papers with code
Estimating Post-Synaptic Effects for Online Training of Feed-Forward SNNs
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
In this paper, we propose a technique that leverages inexpensive Hadamard transforms to enable low-precision training with only integer matrix multiplications.
Augmenting Hessians with Inter-Layer Dependencies for Mixed-Precision Post-Training Quantization
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.
NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems
The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings.
The Hardware Impact of Quantization and Pruning for Weights in Spiking Neural Networks
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.
Mixed Precision Post Training Quantization of Neural Networks with Sensitivity Guided Search
In this paper, we propose a method to efficiently determine quantization configurations of different tensors in ML models using post-training mixed precision quantization.
Edge Inference with Fully Differentiable Quantized Mixed Precision Neural Networks
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.
Memory Organization for Energy-Efficient Learning and Inference in Digital Neuromorphic Accelerators
The energy efficiency of neuromorphic hardware is greatly affected by the energy of storing, accessing, and updating synaptic parameters.
