Related papers: A Constant-Time Implementation Methodology for Act…

In this paper, we show that, under mild assumptions, input-output behavior of a continous-time recurrent neural network (RNN) can be represented by a rational or polynomial nonlinear system. The assumptions concern the activation function…

We propose a data-driven method for synthesizing a static analyzer to detect side-channel information leaks in cryptographic software. Compared to the conventional way of manually crafting such a static analyzer, which can be labor…

The superconducting LHC magnets are coupled with an electronic monitoring system which records and analyses voltage time series reflecting their performance. A currently used system is based on a range of preprogrammed triggers which…

We present many new results related to reliable (interactive) communication over insertion-deletion channels. Synchronization errors, such as insertions and deletions, strictly generalize the usual symbol corruption errors and are much…

Information leakage can have dramatic consequences on systems security. Among harmful information leaks, the timing information leakage is the ability for an attacker to deduce internal information depending on the system execution time. We…

Side-channel attacks are important security challenges as they reveal sensitive information about on-chip activities. Among such attacks, the thermal side-channel has been shown to disclose the activities of key functional blocks and even…

Multi-process concurrency is effective in improving program efficiency and maximizing CPU utilization. The correct execution of concurrency is ensured by the mutual exclusion and synchronization mechanism (MESM) that manages the shared…

We introduce Active Tuning, a novel paradigm for optimizing the internal dynamics of recurrent neural networks (RNNs) on the fly. In contrast to the conventional sequence-to-sequence mapping scheme, Active Tuning decouples the RNN's…

Spiking neural networks (SNNs) are a promising candidate for biologically-inspired and energy efficient computation. However, their simulation is notoriously time consuming, and may be seen as a bottleneck in developing competitive training…

We claim that existing techniques and tools for generating and verifying constant-time code are incomplete, since they rely on assumptions that compiler optimization passes do not break constant-timeness or that certain operations execute…

We analyze a simple one-hidden-layer neural network with ReLU activation functions and fixed biases, with one-dimensional input and output. We study both continuous and discrete versions of the model, and we rigorously prove the convergence…

In this paper, we introduce a deep learning aided constraint encoding method to tackle the frequency-constraint microgrid scheduling problem. The nonlinear function between system operating condition and frequency nadir is approximated by…

There has been an increasing interest in using neural networks in closed-loop control systems to improve performance and reduce computational costs for on-line implementation. However, providing safety and stability guarantees for these…

Avoiding access conflicts is a major challenge in the design of multi-threaded programs. In the context of real-time systems, the absence of conflicts can be guaranteed by ensuring that no two potentially conflicting accesses are ever…

The wide deployment of Large Language Models (LLMs) has given rise to strong demands for optimizing their inference performance. Today's techniques serving this purpose primarily focus on reducing latency and improving throughput through…

Whereas contemporary Error Correcting Codes (ECC) designs occupy a significant fraction of total die area in chip-multiprocessors (CMPs), approaches to deal with the vulnerability increase of CMP architecture against Single Event Upsets…

Owing to their superior modeling capabilities, gated Recurrent Neural Networks, such as Gated Recurrent Units (GRUs) and Long Short-Term Memory networks (LSTMs), have become popular tools for learning dynamical systems. This paper aims to…

Existing techniques used for intrusion detection do not fully utilize the intrinsic properties of embedded systems. In this paper, we propose a lightweight method for detecting anomalous executions using a distribution of system call…

With the increased interest in artificial intelligence, Machine Learning as a Service provides the infrastructure in the Cloud for easy training, testing, and deploying models. However, these systems have a major privacy issue: uploading…

As intelligent computing devices increasingly integrate into human life, ensuring the functional safety of the corresponding electronic chips becomes more critical. A key metric for functional safety is achieving a sufficient fault…