Fast and Efficient Information Transmission with Burst Spikes in Deep Spiking Neural Networks

The 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.Comment: Accepted to DAC 201

Can We Utilize Pre-trained Language Models within Causal Discovery Algorithms?

Scaling laws have allowed Pre-trained Language Models (PLMs) into the field of causal reasoning. Causal reasoning of PLM relies solely on text-based descriptions, in contrast to causal discovery which aims to determine the causal relationships between variables utilizing data. Recently, there has been current research regarding a method that mimics causal discovery by aggregating the outcomes of repetitive causal reasoning, achieved through specifically designed prompts. It highlights the usefulness of PLMs in discovering cause and effect, which is often limited by a lack of data, especially when dealing with multiple variables. Conversely, the characteristics of PLMs which are that PLMs do not analyze data and they are highly dependent on prompt design leads to a crucial limitation for directly using PLMs in causal discovery. Accordingly, PLM-based causal reasoning deeply depends on the prompt design and carries out the risk of overconfidence and false predictions in determining causal relationships. In this paper, we empirically demonstrate the aforementioned limitations of PLM-based causal reasoning through experiments on physics-inspired synthetic data. Then, we propose a new framework that integrates prior knowledge obtained from PLM with a causal discovery algorithm. This is accomplished by initializing an adjacency matrix for causal discovery and incorporating regularization using prior knowledge. Our proposed framework not only demonstrates improved performance through the integration of PLM and causal discovery but also suggests how to leverage PLM-extracted prior knowledge with existing causal discovery algorithms

Real-Time Anomalous Branch Behavior Inference with a GPU-inspired Engine for Machine Learning Models

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.N