While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation, and the vision of the Internet of Things fuel the interest in resource-efficient approaches. These approaches aim for a carefully chosen trade-off between performance and resource consumption in terms of computation and energy. The development of such approaches is among the major challenges in current machine learning research and key to ensure a smooth transition of machine learning technology from a scientific environment with virtually unlimited computing resources into everyday's applications. In this article, we provide an overview of the current state of the art of machine learning techniques facilitating these real-world requirements. In particular, we focus on resource-efficient inference based on deep neural networks (DNNs), the predominant machine learning models of the past decade. We give a comprehensive overview of the vast literature that can be mainly split into three non-mutually exclusive categories: (i) quantized neural networks, (ii) network pruning, and (iii) structural efficiency. These techniques can be applied during training or as post-processing, and they are widely used to reduce the computational demands in terms of memory footprint, inference speed, and energy efficiency. We also briefly discuss different concepts of embedded hardware for DNNs and their compatibility with machine learning techniques as well as potential for energy and latency reduction. We substantiate our discussion with experiments on well-known benchmark data sets using compression techniques (quantization, pruning) for a set of resource-constrained embedded systems, such as CPUs, GPUs and FPGAs. The obtained results highlight the difficulty of finding good trade-offs between resource efficiency and prediction quality.

Modern musical source separation systems based on deep neural networks reach unprecedented levels of separation quality. However, harnessing the power of these large-scale models in typical audio production environments, which frequently offer only limited computing resources while demanding real-time processing, remains challenging. We extend the multi-scaled DenseNet in several aspects to facilitate real-time source separation scenarios. Specifically, we reduce the computational requirements by inferring Mel-scaled masks and decrease the model size via effective use of bottleneck layers, while improving performance using a deep clustering objective. In addition, we are able to further increase the model efficiency by applying parameterized structured pruning of convolutional weights without any significant impact on the separation performance. We significantly reduce the model size and increase the computational efficiency by a factor of 1.6 and 4.3, respectively, while maintaining the separation performance.

We present two methods to reduce the complexity of Bayesian network (BN) classifiers. First, we introduce quantization-aware training using the straight-through gradient estimator to quantize the parameters of BNs to few bits. Second, we extend a recently proposed differentiable tree-augmented naive Bayes (TAN) structure learning approach by also considering the model size. Both methods are motivated by recent developments in the deep learning community, and they provide effective means to trade off between model size and prediction accuracy, which is demonstrated in extensive experiments. Furthermore, we contrast quantized BN classifiers with quantized deep neural networks (DNNs) for small-scale scenarios which have hardly been investigated in the literature. We show Pareto optimal models with respect to model size, number of operations, and test error and find that both model classes are viable options.

We propose binary neural networks (BNN s) for acoustic beamforming. This makes the speech enhancement approach resource efficient and applicable for embedded applications. Using CHiME4 data, we use BNN s to estimate the speech presence probability mask for GEV-PAN beamformers. By doing so, we achieve audio quality and ASR scores on par to single-precision deep neural networks (DNNs), while the computational requirements and the memory footprint are significantly reduced.