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Found 7 papers, 3 papers with code
The Wyner Variational Autoencoder for Unsupervised Multi-Layer Wireless Fingerprinting
Our information-theoretic approach can be extended to supervised and semi-supervised settings with straightforward derivations.
NITI: Training Integer Neural Networks Using Integer-only Arithmetic
While integer arithmetic has been widely adopted for improved performance in deep quantized neural network inference, training remains a task primarily executed using floating point arithmetic.
MajorityNets: BNNs Utilising Approximate Popcount for Improved Efficiency
Binarized neural networks (BNNs) have shown exciting potential for utilising neural networks in embedded implementations where area, energy and latency constraints are paramount.
AddNet: Deep Neural Networks Using FPGA-Optimized Multipliers
Low-precision arithmetic operations to accelerate deep-learning applications on field-programmable gate arrays (FPGAs) have been studied extensively, because they offer the potential to save silicon area or increase throughput.
Unrolling Ternary Neural Networks
The computational complexity of neural networks for large scale or real-time applications necessitates hardware acceleration.
SYQ: Learning Symmetric Quantization For Efficient Deep Neural Networks
An efficient way to reduce this complexity is to quantize the weight parameters and/or activations during training by approximating their distributions with a limited entry codebook.
Compressing Low Precision Deep Neural Networks Using Sparsity-Induced Regularization in Ternary Networks
A low precision deep neural network training technique for producing sparse, ternary neural networks is presented.
