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Found 12 papers, 5 papers with code
Examining the Role and Limits of Batchnorm Optimization to Mitigate Diverse Hardware-noise in In-memory Computing
In-Memory Computing (IMC) platforms such as analog crossbars are gaining focus as they facilitate the acceleration of low-precision Deep Neural Networks (DNNs) with high area- & compute-efficiencies.
Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systems
In this paper, we propose DC-NAS -- a divide-and-conquer approach that performs supernet-based Neural Architecture Search (NAS) in a federated system by systematically sampling the search space.
Exploring Temporal Information Dynamics in Spiking Neural Networks
After training, we observe that information becomes highly concentrated in earlier few timesteps, a phenomenon we refer to as temporal information concentration.
Exploring Lottery Ticket Hypothesis in Spiking Neural Networks
To scale up a pruning technique towards deep SNNs, we investigate Lottery Ticket Hypothesis (LTH) which states that dense networks contain smaller subnetworks (i. e., winning tickets) that achieve comparable performance to the dense networks.
MIME: Adapting a Single Neural Network for Multi-task Inference with Memory-efficient Dynamic Pruning
Recent years have seen a paradigm shift towards multi-task learning.
Addressing Client Drift in Federated Continual Learning with Adaptive Optimization
However, there is little attention towards additional challenges emerging when federated aggregation is performed in a continual learning system.
Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?
Then, we measure the robustness of the coding techniques on two adversarial attack methods.
Neural Architecture Search for Spiking Neural Networks
Interestingly, SNASNet found by our search algorithm achieves higher performance with backward connections, demonstrating the importance of designing SNN architecture for suitably using temporal information.
Federated Learning with Spiking Neural Networks
To validate the proposed federated learning framework, we experimentally evaluate the advantages of SNNs on various aspects of federated learning with CIFAR10 and CIFAR100 benchmarks.
PrivateSNN: Privacy-Preserving Spiking Neural Networks
2) Class leakage is caused when class-related features can be reconstructed from network parameters.
Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks
Additionally, we find that integrating AD based quantization with AD based pruning (both conducted during training) yields up to ~198x and ~44x energy reductions for VGG19 and ResNet18 architectures respectively on PIM platform compared to baseline 16-bit precision, unpruned models.
Activation Density driven Energy-Efficient Pruning in Training
Neural network pruning with suitable retraining can yield networks with considerably fewer parameters than the original with comparable degrees of accuracy.
