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Register a new userOn the Quantization of Cellular Neural Networks for Cyber-Physical Systems
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Xiaowei Xu
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Cyber-Physical Systems (CPSs) have been pervasive including smart grid, autonomous automobile systems, medical monitoring, process control systems, robotics systems, and automatic pilot avionics. As usually implemented on embedded devices, CPS is typically constrained by computation capacity and energy consumption. In some CPS applications such as telemedicine and advanced driving assistance system (ADAS), data processing on the embedded devices is preferred due to security/safety and real-time requirement. Therefore, high efficiency is highly desirable for such CPS applications. In this paper we present CeNN quantization for high-efficient processing for CPS applications, particularly telemedicine and ADAS applications. We systematically put forward powers-of-two based incremental quantization of CeNNs for efficient hardware implementation. The incremental quantization contains iterative procedures including parameter partition, parameter quantization, and re-training. We propose five different strategies including random strategy, pruning inspired strategy, weighted pruning inspired strategy, nearest neighbor strategy, and weighted nearest neighbor strategy. Experimental results show that our approach can achieve a speedup up to 7.8x with no performance loss compared with the state-of-the-art FPGA solutions for CeNNs.
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Graph neural networks (GNNs) have demonstrated strong performance on a wide variety of tasks due to their ability to model non-uniform structured data. Despite their promise, there exists little research exploring methods to make them more efficient at inference time. In this work, we explore the viability of training quantized GNNs, enabling the usage of low precision integer arithmetic during inference. We identify the sources of error that uniquely arise when attempting to quantize GNNs, and propose an architecturally-agnostic method, Degree-Quant, to improve performance over existing quantization-aware training baselines commonly used on other architectures, such as CNNs. We validate our method on six datasets and show, unlike previous attempts, that models generalize to unseen graphs. Models trained with Degree-Quant for INT8 quantization perform as well as FP32 models in most cases; for INT4 models, we obtain up to 26% gains over the baselines. Our work enables up to 4.7x speedups on CPU when using INT8 arithmetic.
Deep Neural Networks (DNNs) are popularly used for implementing autonomy related tasks in automotive Cyber-Physical Systems (CPSs). However, these networks have been shown to make erroneous predictions to anomalous inputs, which manifests either due to Out-of-Distribution (OOD) data or adversarial attacks. To detect these anomalies, a separate DNN called assurance monitor is often trained and used in parallel to the controller DNN, increasing the resource burden and latency. We hypothesize that a single network that can perform controller predictions and anomaly detection is necessary to reduce the resource requirements. Deep-Radial Basis Function (RBF) networks provide a rejection class alongside the class predictions, which can be utilized for detecting anomalies at runtime. However, the use of RBF activation functions limits the applicability of these networks to only classification tasks. In this paper, we show how the deep-RBF network can be used for detecting anomalies in CPS regression tasks such as continuous steering predictions. Further, we design deep-RBF networks using popular DNNs such as NVIDIA DAVE-II, and ResNet20, and then use the resulting rejection class for detecting adversarial attacks such as a physical attack and data poison attack. Finally, we evaluate these attacks and the trained deep-RBF networks using a hardware CPS testbed called DeepNNCar and a real-world German Traffic Sign Benchmark (GTSB) dataset. Our results show that the deep-RBF networks can robustly detect these attacks in a short time without additional resource requirements.
With the increasing popularity of graph-based learning, Graph Neural Networks (GNNs) win lots of attention from the research and industry field because of their high accuracy. However, existing GNNs suffer from high memory footprints (e.g., node embedding features). This high memory footprint hurdles the potential applications towards memory-constrained devices, such as the widely-deployed IoT devices. To this end, we propose a specialized GNN quantization scheme, SGQuant, to systematically reduce the GNN memory consumption. Specifically, we first propose a GNN-tailored quantization algorithm design and a GNN quantization fine-tuning scheme to reduce memory consumption while maintaining accuracy. Then, we investigate the multi-granularity quantization strategy that operates at different levels (components, graph topology, and layers) of GNN computation. Moreover, we offer an automatic bit-selecting (ABS) to pinpoint the most appropriate quantization bits for the above multi-granularity quantizations. Intensive experiments show that SGQuant can effectively reduce the memory footprint from 4.25x to 31.9x compared with the original full-precision GNNs while limiting the accuracy drop to 0.4% on average.
Many complex cyber-physical systems can be modeled as heterogeneous components interacting with each other in real-time. We assume that the correctness of each component can be specified as a requirement satisfied by the output signals produced by the component, and that such an output guarantee is expressed in a real-time temporal logic such as Signal Temporal Logic (STL). In this paper, we hypothesize that a large subset of input signals for which the corresponding output signals satisfy the output requirement can also be compactly described using an STL formula that we call the environment assumption. We propose an algorithm to mine such an environment assumption using a supervised learning technique. Essentially, our algorithm treats the environment assumption as a classifier that labels input signals as good if the corresponding output signal satisfies the output requirement, and as bad otherwise. Our learning method simultaneously learns the structure of the STL formula as well as the values of the numeric constants appearing in the formula. To achieve this, we combine a procedure to systematically enumerate candidate Parametric STL (PSTL) formulas, with a decision-tree based approach to learn parameter values. We demonstrate experimental results on real world data from several domains including transportation and health care.
This paper addresses a challenging problem - how to reduce energy consumption without incurring performance drop when deploying deep neural networks (DNNs) at the inference stage. In order to alleviate the computation and storage burdens, we propose a novel dataflow-based joint quantization approach with the hypothesis that a fewer number of quantization operations would incur less information loss and thus improve the final performance. It first introduces a quantization scheme with efficient bit-shifting and rounding operations to represent network parameters and activations in low precision. Then it restructures the network architectures to form unified modules for optimization on the quantized model. Extensive experiments on ImageNet and KITTI validate the effectiveness of our model, demonstrating that state-of-the-art results for various tasks can be achieved by this quantized model. Besides, we designed and synthesized an RTL model to measure the hardware costs among various quantization methods. For each quantization operation, it reduces area cost by about 15 times and energy consumption by about 9 times, compared to a strong baseline.
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