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Found 10 papers, 3 papers with code
Integrating Deep Learning and Synthetic Biology: A Co-Design Approach for Enhancing Gene Expression via N-terminal Coding Sequences
The NCS optimization problem is to find an NCS that maximizes gene expression.
HyperSNN: A new efficient and robust deep learning model for resource constrained control applications
In light of the increasing adoption of edge computing in areas such as intelligent furniture, robotics, and smart homes, this paper introduces HyperSNN, an innovative method for control tasks that uses spiking neural networks (SNNs) in combination with hyperdimensional computing.
DeepFire2: A Convolutional Spiking Neural Network Accelerator on FPGAs
Brain-inspired spiking neural networks (SNNs) replace the multiply-accumulate operations of traditional neural networks by integrate-and-fire neurons, with the goal of achieving greater energy efficiency.
Efficient Hyperdimensional Computing
Hyperdimensional computing (HDC) is a method to perform classification that uses binary vectors with high dimensions and the majority rule.
Desire Backpropagation: A Lightweight Training Algorithm for Multi-Layer Spiking Neural Networks based on Spike-Timing-Dependent Plasticity
Spiking neural networks (SNNs) are a viable alternative to conventional artificial neural networks when resource efficiency and computational complexity are of importance.
Low Latency Conversion of Artificial Neural Network Models to Rate-encoded Spiking Neural Networks
The maximum number of spikes in this time window is also the latency of the network in performing a single inference, as well as determines the overall energy efficiency of the model.
Optimizing for In-memory Deep Learning with Emerging Memory Technology
In-memory deep learning has already demonstrated orders of magnitude higher performance density and energy efficiency.
BLUnet: Arithmetic-free Inference with Bit-serialised Table Lookup Operation for Efficient Deep Neural Networks
In this paper, we propose BLUnet, a table lookup-based DNN model with bit-serialized input to overcome this challenge.
DTNN: Energy-efficient Inference with Dendrite Tree Inspired Neural Networks for Edge Vision Applications
DTNN achieved significant energy saving (19. 4X and 64. 9X improvement on ResNet-18 and VGG-11 with ImageNet, respectively) with negligible loss of accuracy.
Shenjing: A low power reconfigurable neuromorphic accelerator with partial-sum and spike networks-on-chip
We show that conventional artificial neural networks (ANN) such as multilayer perceptron, convolutional neural networks, as well as the latest residual neural networks can be mapped successfully onto Shenjing, realizing ANNs with SNN's energy efficiency.
