Search Results for author:
Found 12 papers, 2 papers with code
Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networks
Utilizing these principles, we formulated a kernel for sparse matrix multiplication (SpMM) in GCNs that employs block-level partitioning and combined warp strategy.
Neuromorphic Online Learning for Spatiotemporal Patterns with a Forward-only Timeline
The algorithm not only learns the synaptic weight but also adapts the temporal filters associated to the synapses.
Neurogenesis Dynamics-inspired Spiking Neural Network Training Acceleration
Experimental results show that NDSNN achieves up to 20. 52\% improvement in accuracy on Tiny-ImageNet using ResNet-19 (with a sparsity of 99\%) as compared to other SOTA methods (e. g., Lottery Ticket Hypothesis (LTH), SET-SNN, RigL-SNN).
Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples
First, we show that successful white-box adversarial attacks on SNNs are highly dependent on the underlying surrogate gradient technique, even in the case of adversarially trained SNNs.
In-Hardware Learning of Multilayer Spiking Neural Networks on a Neuromorphic Processor
Although widely used in machine learning, backpropagation cannot directly be applied to SNN training and is not feasible on a neuromorphic processor that emulates biological neuron and synapses.
Neuromorphic Algorithm-hardware Codesign for Temporal Pattern Learning
This circuit implementation of the neuron model is simulated to demonstrate its ability to react to temporal spiking patterns with an adaptive threshold.
FTRANS: Energy-Efficient Acceleration of Transformers using FPGA
In natural language processing (NLP), the "Transformer" architecture was proposed as the first transduction model replying entirely on self-attention mechanisms without using sequence-aligned recurrent neural networks (RNNs) or convolution, and it achieved significant improvements for sequence to sequence tasks.
Multivariate Time Series Classification Using Spiking Neural Networks
A training algorithm to classify spatial temporal patterns is also proposed.
MAGNet: Multi-Region Attention-Assisted Grounding of Natural Language Queries at Phrase Level
Grounding free-form textual queries necessitates an understanding of these textual phrases and its relation to the visual cues to reliably reason about the described locations.
GISNet: Graph-Based Information Sharing Network For Vehicle Trajectory Prediction
The trajectory prediction is a critical and challenging problem in the design of an autonomous driving system.
Exploiting Neuron and Synapse Filter Dynamics in Spatial Temporal Learning of Deep Spiking Neural Network
A bio-plausible SNN model with spatial-temporal property is a complex dynamic system.
High-Level Plan for Behavioral Robot Navigation with Natural Language Directions and R-NET
When the navigational environment is known, it can be represented as a graph where landmarks are nodes, the robot behaviors that move from node to node are edges, and the route is a set of behavioral instructions.
