We consider using a battery storage system simultaneously for peak shaving and frequency regulation through a joint optimization framework which captures battery degradation, operational constraints and uncertainties in customer load and regulation signals. Under this framework, using real data we show the electricity bill of users can be reduced by up to 15%. Furthermore, we demonstrate that the saving from joint optimization is often larger than the sum of the optimal savings when the battery is used for the two individual applications. A simple threshold real-time algorithm is proposed and achieves this super-linear gain. Compared to prior works that focused on using battery storage systems for single applications, our results suggest that batteries can achieve much larger economic benefits than previously thought if they jointly provide multiple services.

Can we reduce the search cost of Neural Architecture Search (NAS) from days down to only a few hours? NAS methods automate the design of Convolutional Networks (ConvNets) under hardware constraints and they have emerged as as a key component of cloud AutoML services. However, the NAS problem remains challenging due to the combinatorially large design space and the significant search time (at least 200 GPU-hours). In this work, we alleviate the NAS search cost down to less than 3 hours, while achieving state-of-the-art image classification results under mobile latency constraints. We propose a novel differentiable NAS formulation, namely Single-Path NAS, that uses one single-path over-parameterized ConvNet to encode all architectural decisions based on shared convolutional kernel parameters, hence drastically decreasing the search overhead. Single-Path NAS achieves state-of-the-art top-1 ImageNet accuracy (75.62%), hence outperforming existing mobile NAS methods in similar latency settings (∼80ms). In particular, we enhance the accuracy-runtime trade-off in differentiable NAS by treating the Squeeze-and-Excitation path as a fully searchable operation with our novel single-path encoding. Our method has an overall cost of only 8 epochs (24 TPU-hours), which is up to 5,000× faster compared to prior work. Moreover, we study how different NAS formulation choices affect the performance of the designed ConvNets. Furthermore, we exploit the efficiency of our method to answer an interesting question: instead of empirically tuning the hyperparameters of the NAS solver (as in prior work), can we automatically find the hyperparameter values that yield the desired accuracy-runtime trade-off (e.g., target runtime for different platforms)? We view our extensive experimental results as a valuable exploration for NAS-based cloud AutoML services, and we open-source our entire codebase at: https://github.com/dstamoulis/single-path-nas.

In recent years, the neural network (NN) has shown its great potential in image recognition tasks of autonomous driving systems, such as traffic sign recognition, pedestrian detection, etc. However, theoretically well-trained NNs usually fail their performance when facing real-world scenarios. For example, adverse real-world conditions, e.g., bad weather and lighting conditions, can introduce different physical variations and cause considerable accuracy degradation. As for now, the generalization capability of NNs is still one of the most critical challenges for the autonomous driving system. To facilitate the robust image recognition tasks, in this work, we build the RobuTS dataset: a comprehensive Robust Traffic Sign Recognition dataset, which includes images with different environmental variations, e.g., rain, fog, darkening, and blurring. Then to enhance the NN’s generalization capability, we propose two generalization-enhanced training schemes: 1) REIN for robust training without data in adverse scenarios and 2) Self-Teaching (ST) for robust training with unlabeled adverse data. The great advantages of such two training schemes are they are data-free ( REIN ) and label-free ( ST ), thus effectively reducing the huge human efforts/cost of on-road driving data collection, as well as the expensive manual data annotation. We conduct extensive experiments to validate our methods’ performance on both classification and detection tasks. For classification tasks, our proposed training algorithms could consistently improve model performance by +15%–25% ( REIN ) and +16%–30% ( ST ) in all adverse scenarios of our RobuTS datasets. For detection tasks, our ST could also improve the detector’s performance by +10.1 mean average precision (mAP) on Foggy-Cityscapes, outperforming previous state-of-the-art works by +2.2 mAP.

Continual learning (CL) trains NN models incrementally from a continuous stream of tasks. To remember previously learned knowledge, prior studies store old samples over a memory hierarchy and replay them when new tasks arrive. Edge devices that adopt CL to preserve data privacy are typically energy-sensitive and thus require high model accuracy while not compromising energy efficiency, i.e., cost-effectiveness. Our work is the first to explore the design space of hierarchical memory replay-based CL to gain insights into achieving cost-effectiveness on edge devices. We present Miro, a novel system runtime that carefully integrates our insights into the CL framework by enabling it to dynamically configure the CL system based on resource states for the best cost-effectiveness. To reach this goal, Miro also performs online profiling on parameters with clear accuracy-energy trade-offs and adapts to optimal values with low overhead. Extensive evaluations show that Miro significantly outperforms baseline systems we build for comparison, consistently achieving higher cost-effectiveness.

Deep Neural Networks (DNNs) achieved great cognitive performance at the expense of considerable computation workload. To relieve the computational burden, many optimization works are developed to reduce the model redundancy by identifying and removing insignificant model components, such as weight sparsity and filter pruning methods. However, these works only evaluate model components’ static significance with parameter information, ignoring their dynamic interaction with external inputs. Specifically, due to the difference in per-input features, the model components’ significance can dynamically change, and thus the static methods can only achieve sub-optimal performance. Focusing on this aspect, we propose a dynamic DNN optimization framework in this work. Based on the neural network attention mechanism, we propose a comprehensive dynamic optimization framework including (1) testing-phase dynamic feature map pruning; (2) training-phase optimization by training with targeted dropout; and (3) deployment-phase one-for-all model adaptability enhancement. By providing a holistic dynamic testing, training, and deployment co-optimization framework, our work has following benefits: First, it can accurately identify and aggressively remove per-input feature redundancy by considering the model-input interaction and involving the channel/column-wise pruning flexibility; Meanwhile, the training-testing co-optimization favors the dynamic pruning and helps maintain the model accuracy even with very high feature pruning ratio. Finally, the deployment enhancement provides one unified one-for-all model to support full-spectrum feature sparsity ratios. The unified model can be dynamically reconfigured to meet different resource budgets without any retraining cost, and thus provide significant deployment flexibility. Extensive experiments show that our method could bring 37.4% 54.5% FLOPs reduction with negligible accuracy drop on various of test benchmarks. Meanwhile, the one-for-all deployment optimization enables us to use one model to support at most ten different resource constraints without any retraining cost.