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Found 8 papers, 0 papers with code
Gradient-based Bit Encoding Optimization for Noise-Robust Binary Memristive Crossbar
In addition, we propose Gradient-based Bit Encoding Optimization (GBO) which optimizes a different number of pulses at each layer, based on our in-depth analysis that each layer has a different level of noise sensitivity.
Scalable Smartphone Cluster for Deep Learning
We implemented large-batch synchronous training of DNNs based on Caffe, a deep learning library.
T2FSNN: Deep Spiking Neural Networks with Time-to-first-spike Coding
Spiking neural networks (SNNs) have gained considerable interest due to their energy-efficient characteristics, yet lack of a scalable training algorithm has restricted their applicability in practical machine learning problems.
Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection
Over the past decade, deep neural networks (DNNs) have demonstrated remarkable performance in a variety of applications.
Fast and Efficient Information Transmission with Burst Spikes in Deep Spiking Neural Networks
The spiking neural networks (SNNs) are considered as one of the most promising artificial neural networks due to their energy efficient computing capability.
Energy-Efficient Inference Accelerator for Memory-Augmented Neural Networks on an FPGA
Memory-augmented neural networks (MANNs) are designed for question-answering tasks.
Quantized Memory-Augmented Neural Networks
In this paper, we identify memory addressing (specifically, content-based addressing) as the main reason for the performance degradation and propose a robust quantization method for MANNs to address the challenge.
Near-Data Processing for Differentiable Machine Learning Models
The second is the popularity of NAND flash-based solid-state drives (SSDs) containing multicore processors that can accommodate extra computation for data processing.
