Differentiable Transportation Pruning

Deep learning algorithms are increasingly employed at the edge. However, edge devices are resource constrained and thus require efficient deployment of deep neural networks. Pruning methods are a key tool for edge deployment as they can improve storage, compute, memory bandwidth, and energy usage. In this paper we propose a novel accurate pruning technique that allows precise control over the output network size. Our method uses an efficient optimal transportation scheme which we make end-to-end differentiable and which automatically tunes the exploration-exploitation behavior of the algorithm to find accurate sparse sub-networks. We show that our method achieves state-of-the-art performance compared to previous pruning methods on 3 different datasets, using 5 different models, across a wide range of pruning ratios, and with two types of sparsity budgets and pruning granularities.

FQ-Conv: Fully Quantized Convolution for Efficient and Accurate Inference

Deep neural networks (DNNs) can be made hardware-efficient by reducing the numerical precision of the weights and activations of the network and by improving the network's resilience to noise. However, this gain in efficiency often comes at the cost of significantly reduced accuracy. In this paper, we present a novel approach to quantizing convolutional neural network. The resulting networks perform all computations in low-precision, without requiring higher-precision BN and nonlinearities, while still being highly accurate. To achieve this result, we employ a novel quantization technique that learns to optimally quantize the weights and activations of the network during training. Additionally, to enhance training convergence we use a new training technique, called gradual quantization. We leverage the nonlinear and normalizing behavior of our quantization function to effectively remove the higher-precision nonlinearities and BN from the network. The resulting convolutional layers are fully quantized to low precision, from input to output, ideal for neural network accelerators on the edge. We demonstrate the potential of this approach on different datasets and networks, showing that ternary-weight CNNs with low-precision in- and outputs perform virtually on par with their full-precision equivalents. Finally, we analyze the influence of noise on the weights, activations and convolution outputs (multiply-accumulate, MAC) and propose a strategy to improve network performance under noisy conditions.