Spiking neural networks (SNNs) that mimic information transmission in the brain can energy-efficiently process spatio-temporal information through discrete and sparse spikes, thereby receiving considerable attention. To improve accuracy and energy efficiency of SNNs, most previous studies have focused solely on training methods, and the effect of architecture has rarely been studied. We investigate the design choices used in the previous studies in terms of the accuracy and number of spikes and figure out that they are not best-suited for SNNs. To further improve the accuracy and reduce the spikes generated by SNNs, we propose a spike-aware neural architecture search framework called AutoSNN. We define a search space consisting of architectures without undesirable design choices. To enable the spike-aware architecture search, we introduce a fitness that considers both the accuracy and number of spikes. AutoSNN successfully searches for SNN architectures that outperform hand-crafted SNNs in accuracy and energy efficiency. We thoroughly demonstrate the effectiveness of AutoSNN on various datasets including neuromorphic datasets.

Deep neural networks continue to awe the world with their remarkable performance. Their predictions, however, are prone to be corrupted by adversarial examples that are imperceptible to humans. Current efforts to improve the robustness of neural networks against adversarial examples are focused on developing robust training methods, which update the weights of a neural network in a more robust direction. In this work, we take a step beyond training of the weight parameters and consider the problem of designing an adversarially robust neural architecture with high intrinsic robustness. We propose AdvRush, a novel adversarial robustness-aware neural architecture search algorithm, based upon a finding that independent of the training method, the intrinsic robustness of a neural network can be represented with the smoothness of its input loss landscape. Through a regularizer that favors a candidate architecture with a smoother input loss landscape, AdvRush successfully discovers an adversarially robust neural architecture. Along with a comprehensive theoretical motivation for AdvRush, we conduct an extensive amount of experiments to demonstrate the efficacy of AdvRush on various benchmark datasets. Notably, on CIFAR-10, AdvRush achieves 55.91% robust accuracy under FGSM attack after standard training and 50.04% robust accuracy under AutoAttack after 7-step PGD adversarial training.

Despite the increasing interest in neural architecture search (NAS), the significant computational cost of NAS is a hindrance to researchers. Hence, we propose to reduce the cost of NAS using proxy data, i.e., a representative subset of the target data, without sacrificing search performance. Even though data selection has been used across various fields, our evaluation of existing selection methods for NAS algorithms offered by NAS-Bench-1shot1 reveals that they are not always appropriate for NAS and a new selection method is necessary. By analyzing proxy data constructed using various selection methods through data entropy, we propose a novel proxy data selection method tailored for NAS. To empirically demonstrate the effectiveness, we conduct thorough experiments across diverse datasets, search spaces, and NAS algorithms. Consequently, NAS algorithms with the proposed selection discover architectures that are competitive with those obtained using the entire dataset. It significantly reduces the search cost: executing DARTS with the proposed selection requires only 40 minutes on CIFAR-10 and 7.5 hours on ImageNet with a single GPU. Additionally, when the architecture searched on ImageNet using the proposed selection is inversely transferred to CIFAR-10, a state-of-the-art test error of 2.4% is yielded. Our code is available at https://github.com/nabk89/NAS-with-Proxy-data.

Spiking neural networks (SNNs) are considered as one of the most promising artificial neural networks due to their energy-efficient computing capability. Recently, conversion of a trained deep neural network to an SNN has improved the accuracy of deep SNNs. However, most of the previous studies have not achieved satisfactory results in terms of inference speed and energy efficiency. In this paper, we propose a fast and energy-efficient information transmission method with burst spikes and hybrid neural coding scheme in deep SNNs. Our experimental results showed the proposed methods can improve inference energy efficiency and shorten the latency.

Attacks on embedded devices are likely to occur any time in unexpected manners. Thus, the defense systems based on fixed sets of rules will easily be subverted by such unexpected, unknown attacks. Learning-based anomaly detection may potentially prevent new unknown zero-day attacks by leveraging the capability of machine learning (ML) to learn the intricate true nature of software hidden within raw information. This paper introduces our work to develop an MPSoC, called RTAD, which can efficiently support in hardware various ML models that run to detect anomalous behaviors on embedded devices in a real-time fashion, and thus enable the devices to counteract the anomalies in the field. In the IoT era, the importance of security for embedded devices cannot be exaggerated because they will become an enticing target for adversaries as they are being integrated into everyday life to provide users with various services. The above-mentioned potential of learning-based detection is believed to benefit those deployed devices under attacks occurring any time during their field operations in unexpected manners. We hereby assume that ML models are trained with runtime branch information as their data features since a sequence of branches serves as a record of control flow transfers during program execution. In fact, there have been numerous ML studies that examine various types of branches in order to infer (or detect) anomaly in branch behaviors that may be induced by diverse attacks that can cause deviant control flow in software. Our goal of real-time anomalous branch behavior inference poses two challenges to our development of RTAD. Firstly, RTAD must collect and transfer in a timely fashion a sequence of branches as the input to the ML model. Secondly, RTAD must be able to promptly process the delivered branch data with the ML model. To tackle these challenges, we have implemented in RTAD two core components: an input generation module and a GPU-inspired ML processing engine. According to our experiments, RTAD enables various ML models to infer anomaly instantly after the victim program behaves aberrantly as the result of attacks being injected into the system.

Today's datacenters are equipped with diverse computing and storage devices for handling a myriad of data and normally consume a significant amount of electrical energy. This paper proposes a smart grid inspired methodology to monitor and profile the energy consumption of a datacenter, with the aim of providing information useful for reducing the peak power consumption of the datacenter. Our energy measurement platform is named CloudSocket, and each CloudSocket unit can measure the power consumption of an individual computing node and periodically transmit the measurement information wirelessly to the coordinator unit that can manage many Cloud-Sockets simultaneously. We tested our methodology with a 32-node grid system that runs Apache Spark for large-scale data analytics. Analyzing our experimental results reveals how and where the peak power of each node in the grid overlaps, providing opportunities for informative coordination of the computing components for overall power reduction.