Related papers: FlexEmu: Towards Flexible MCU Peripheral Emulation…
In this work we present the Secure Machine, SeM for short, a CPU architecture extension for secure computing. SeM uses a small amount of in-chip additional hardware that monitors key communication channels inside the CPU chip, and only acts…
The exponential growth of Internet of Things (IoT) applications has intensified the demand for efficient, high-throughput, and energy-efficient data processing at the edge. Conventional CPU-centric encryption methods suffer from performance…
We present Emu, a Transformer-based multimodal foundation model, which can seamlessly generate images and texts in multimodal context. This omnivore model can take in any single-modality or multimodal data input indiscriminately (e.g.,…
Microcontroller systems are integral to our daily lives, powering mission-critical applications such as vehicles, medical devices, and industrial control systems. Therefore, it is essential to investigate and outline the challenges…
Educational simulations have long been recognized as powerful tools for enhancing learning outcomes, yet their creation has traditionally required substantial resources and technical expertise. This paper introduces MicroSims a novel…
Deploying neural networks on microcontroller units (MCUs) is critical for edge intelligence but remains challenging due to tight memory, storage, and computation constraints. Existing approaches, such as model compression and hardware-aware…
Large Language Models (LLMs) show promise in generating firmware for embedded systems, but often introduce security flaws and fail to meet real-time performance constraints. This paper proposes a three-phase methodology that combines…
Outer loop tasks such as optimization, uncertainty quantification or inference can easily become intractable when the underlying high-fidelity model is computationally expensive. Similarly, data-driven architectures typically require large…
Microcontroller-based embedded devices are at the core of Internet-of-Things and Cyber-Physical Systems. The security of these devices is of paramount importance. Among the approaches to securing embedded devices, dynamic firmware analysis…
Physically Unclonable Function (PUF) offers a secure and lightweight alternative to traditional cryptography for authentication due to their unique device fingerprint. However, their dependence on specialized hardware hinders their adoption…
Machine learning models deployed on edge devices have enabled numerous exciting new applications, such as humanoid robots, AR glasses, and autonomous vehicles. However, the computing resources available on these edge devices are not…
To achieve real-world functionality, robots must have the ability to carry out decision-making computations. However, soft robots stretch and therefore need a solution other than rigid computers. Examples of embedding computing capacity…
One of the biggest attack surfaces of embedded systems is their network interfaces, which enable communication with other devices. Unlike their general-purpose counterparts, embedded systems are designed for specialized use cases, resulting…
Very deep submicron and nanometer technologies have increased notably integrated circuit (IC) sensitiveness to radiation. Soft errors are currently appearing into ICs working at earth surface. Hardened circuits are currently required in…
Memristive in-memory computing (IMC) has emerged as a promising solution for addressing the bottleneck in the Von Neumann architecture. However, the couplingbetweenthecircuitandalgorithm in IMC makes computing reliability susceptible to…
Firmware serves as the critical interface between hardware and software in computing systems, making any bugs or vulnerabilities particularly dangerous as they can cause catastrophic system failures. While fuzzing is a promising approach…
Foundation models are deep neural networks (such as GPT-5, Gemini~3, and Opus~4) trained on large datasets that can perform diverse downstream tasks -- text and code generation, question answering, summarization, image classification, and…
Flexible robots may overcome some of the industry's major challenges, such as enabling intrinsically safe human-robot collaboration and achieving a higher payload-to-mass ratio. However, controlling flexible robots is complicated due to…
Empirical Risk Minimization (ERM) models often rely on spurious correlations between features and labels during the learning process, leading to shortcut learning behavior that undermines robustness generalization performance. Current…
Processing-in-memory (PIM) solutions vastly accelerate systems by reducing data transfer between computation and memory. Memristors possess a unique property that enables storage and logic within the same device, which is exploited in the…