There exists a gap in terms of the signals provided by pacemakers (i.e., intracardiac electrogram (EGM)) and the signals doctors use (i.e., 12-lead electrocardiogram (ECG)) to diagnose abnormal rhythms. Therefore, the former, even if remotely transmitted, are not sufficient for doctors to provide a precise diagnosis, let alone make a timely intervention. To close this gap and make a heuristic step towards real-time critical intervention in instant response to irregular and infrequent ventricular rhythms, we propose a new framework dubbed RT-RCG to automatically search for (1) efficient Deep Neural Network (DNN) structures and then (2) corresponding accelerators, to enable eal-ime and high-quality econstruction of EG signals from EM signals. Specifically, RT-RCG proposes a new DNN search space tailored for ECG reconstruction from EGM signals, and incorporates a differentiable acceleration search (DAS) engine to efficiently navigate over the large and discrete accelerator design space to generate optimized accelerators. Extensive experiments and ablation studies under various settings consistently validate the effectiveness of our RT-RCG. To the best of our knowledge, RT-RCG is the first to leverage neural architecture search (NAS) to simultaneously tackle both reconstruction efficacy and efficiency.

Race logic, an arrival-time-coded logic family, has demonstrated energy and performance improvements for applications ranging from dynamic programming to machine learning. However, the various mappings of algorithms into hardware rely on researcher ingenuity and result in custom architectures that are difficult to systematize. We propose to associate race logic with the mathematical field of tropical algebra, enabling a more methodical approach toward building temporal circuits. This association between the mathematical primitives of tropical algebra and generalized race logic computations guides the design of temporally coded tropical circuits. It also serves as a framework for expressing high-level timing-based algorithms. This abstraction, when combined with temporal memory, allows for the systematic exploration of race logic-based temporal architectures by making it possible to partition feed-forward computations into stages and organize them into a state machine. We leverage analog memristor-based temporal memories to design such a state machine that operates purely on time-coded wavefronts. We implement a version of Dijkstra's algorithm to evaluate this temporal state machine. This demonstration shows the promise of expanding the expressibility of temporal computing to enable it to deliver significant energy and throughput advantages.

Implantable systems for biomedical research and clinical care are now a flourishing field of activities in academia as well as industrial institutions. The broad field includes experimental explorations in electronics, mechanical, chemical, and biological components and systems, and the combination of all these. Today virtually all implants involve both electronic circuits and micro-electro-mechanical-systems (MEMS). This article offers a very brief glance back at the early history of implant electronics in the period from the 1950s to the 1970s, by employing selected examples from the author's research. This short review also discusses the challenges of implantable electronics at present, and suggests some potentially important trends in the future research and development of implantable microsystems. It is aimed as an introduction of implantable/attached electronic systems to research engineers that are interested in implantable systems as a section of Biomedical Instrumentations.

The wireless implantable/intracavity micromanometer (WIMM) system was designed to fulfill the unmet need for a chronic bladder pressure sensing device in urological fields such as urodynamics for diagnosis and neuromodulation for bladder control. Neuromodulation in particular would benefit from a wireless bladder pressure sensor which could provide real-time pressure feedback to an implanted stimulator, resulting in greater bladder capacity while using less power. The WIMM uses custom integrated circuitry, a MEMS transducer, and a wireless antenna to transmit pressure telemetry at a rate of 10 Hz. Aggressive power management techniques yield an average current draw of 9 A from a 3.6-Volt micro-battery, which minimizes the implant size. Automatic pressure offset cancellation circuits maximize the sensing dynamic range to account for drifting pressure offset due to environmental factors, and a custom telemetry protocol allows transmission with minimum overhead. Wireless operation of the WIMM has demonstrated that the external receiver can receive the telemetry packets, and the low power consumption allows for at least 24 hours of operation with a 4-hour wireless recharge session.

With the rise of Internet of Things (IoT), devices such as smartphones, embedded medical devices, smart home appliances as well as traditional computing platforms such as personal computers and servers have been increasingly targeted with a variety of cyber attacks. Due to limited hardware resources for embedded devices and difficulty in wide-coverage and on-time software updates, software-only cyber defense techniques, such as traditional anti-virus and malware detectors, do not offer a silver-bullet solution. Hardware-based security monitoring and protection techniques, therefore, have gained significant attention. Monitoring devices using side channel leakage information, e.g. power supply variation and electromagnetic (EM) radiation, is a promising avenue that promotes multiple directions in security and trust applications. In this paper, we provide a taxonomy of hardware-based monitoring techniques against different cyber and hardware attacks, highlight the potentials and unique challenges, and display how power-based side-channel instruction-level monitoring can offer suitable solutions to prevailing embedded device security issues. Further, we delineate approaches for future research directions.