Modern healthcare is transforming from hospital-centric to individual-centric systems. Emerging implantable and wearable medical (IWM) devices are integral parts of enabling affordable and accessible healthcare. Early disease diagnosis and preventive measures are possible by continuously monitoring clinically significant physiological parameters. However, most IWM devices are battery-operated, requiring replacement, which interrupts the proper functioning of these devices. For the continuous operation of medical devices for an extended period of time, supplying uninterrupted energy is crucial. A sustainable and health-compatible energy supply will ensure the high-performance real-time functioning of IWM devices and prolong their lifetime. Therefore, harvesting energy from the human body and ambient environment is necessary for enduring precision healthcare and maximizing user comfort. Energy harvesters convert energy from various sources into an equivalent electrical form. This paper presents a state-of-the-art comprehensive review of energy harvesting techniques focusing on medical applications. Various energy harvesting approaches, working principles, and the current state are discussed. In addition, the advantages and limitations of different methods are analyzed and existing challenges and prospects for improvement are outlined. This paper will help with understanding the energy harvesting technologies for the development of high-efficiency, reliable, robust, and battery-free portable medical devices.

This paper presents the design of an optimized hardware-based neural network (NN) called a Shift-Accumulate Binarized Neural Network (SABiNN). SABiNN is employed in detecting respiratory-related diseases such as sleep apnea (SA) among adults. Initially, a 3-hidden layer-based NN model was trained, validated, and tested with open-source apnea PSG datasets collected from the PhysioNET databank. Single lead ECG and pulse oximeter data were collected, pre-processed, and digitized for network training. The NN was later transformed into SABiNN, demonstrating model accuracy of 81.5% (CI: ± 3.5) with an energy efficiency of 5mJ on reprogrammable hardware. The precision rate of the model was further increased by re-designing the XNOR gate of the multiply-accumulate (MAC) operation with NAND gate-based XNOR. This re-design process significantly improved the overall model’s classification and precision. Further expansion of SABiNN was carried out to achieve a higher accuracy rate (over 88%) which was designed on the CMOS platform using a 130 nm open-source process design kit (PDK) developed by Google and Skywater. The proposed model on the CMOS platform utilized a chip area of 0.16 mm ² and showcased an energy efficiency of 1pJ. A total of ~11k CMOS cells with two 16-bit input and one 1-bit output pins were utilized to realize the SABiNN on CMOS. The success of this design technique can be leveraged in developing a fully system-on-a-chip (SoC) integrated wearable system for sleep apnea detection.

Single-photon avalanche diodes (SPADs) belong to a family of avalanche photodiodes (APDs) with single-photon detection capability that operate above the breakdown voltage (i.e., Geiger mode). Design and technology constraints, such as dark current, photon detection probability, and power dissipation, impose inherent device limitations on avalanche photodiodes. Moreover, after the detection of a photon, SPADs require dead time for avalanche quenching and recharge before they can detect another photon. The reduction in dead time results in higher efficiency for photon detection in high-frequency applications. In this work, an electronic interface, based on the pole-zero compensation technique for reducing dead time, was investigated. A nanosecond pulse generator was designed and fabricated to generate pulses of comparable voltage to an avalanche transistor. The quenching time constant (τ_q) is not affected by the compensation capacitance variation, while an increase of about 30% in the τ_q is related to the properties of the specific op-amp used in the design. Conversely, the recovery time was observed to be strongly influenced by the compensation capacitance. Reductions in the recovery time, from 927.3 ns down to 57.6 ns and 9.8 ns, were observed when varying the compensation capacitance in the range of 5–0.1 pF. The experimental results from an SPAD combined with an electronic interface based on an avalanche transistor are in strong accordance, providing similar output pulses to those of an illuminated SPAD.

Non-Volatile Memories (NVMs) such as Resistive RAM (RRAM) are used In neuromorphic systems to Implement high-density and low-power analog synaptic weights. Unfortunately, an RRAM cell can switch its state after reading its content a certain number of times. Such behavior challenges the integrity and program-onee-read-many-times philosophy of implementing machine learning inference on neuromorphic systems, impacting the Quality-of-Serviee (QoS). Elevated temperatures and frequent usage can significantly shorten the number of times an RRAM cell can be reliably read before it becomes absolutely necessary to reprogram. We propose an architectural solution to extend the read endurance of RRAM-based neuromorphic systems. We make two key contributions. First, we formulate the read endurance of an RRAM cell as a function of the programmed synaptic weight and its activation within a machine learning workload. Second, we propose an intelligent workload mapping strategy incorporating the endurance formulation to place the synapses of a machine learning model onto the RRAM cells of the hardware. The objective is to extend the inference lifetime, defined as the number of times the model can be used to generate output (inference) before the trained weights need to be reprogrammed on the RRAM cells of the system. We evaluate our architectural solution with machine learning workloads on a eyele-aeeurate simulator of an RRAM-based neuromorphic system. Our results demonstrate a significant increase in inference lifetime with only a minimal performance impact.

This article presents a system software framework to mitigate read disturb failures of nonvolatile memory (NVM) cells used to implement synapses in neuromorphic fabric. —Fei Su, Intel Corporation

Energy crisis is one of the biggest problems in the third world developing country like Bangladesh. There is a big gap between generation and demand of Electric energy. Almost 50% population of our country is very far away from this blessings. Renewable energy is the only solution of this problem to be an energy efficient developed country. Solar energy is one of the great resources of the renewable energy which can play a crucial role in developing a power deficient country like Bangladesh. This paper provides a proposal of using dual axis solar tracker instead of solar panel. This encompasses a design of ideal solar house model using azimuth-altitude dual axis solar tracker on rooftop. It has been proved through mathematical calculation that the solar energy increases up to 50-60% where dual axis solar tracker is used. Apart from the mentioned design, this paper presents a structure and application of a microcontroller based azimuth-altitude dual axis solar tracker which tracks the solar panel according to the direction of the solar radiation. A built-in ADC converter is used to drive motor circuit. To minimize the power consumption by dual axis solar tracker, we are in favor of using two stepper motor especially during the seasonal change. The proposed model demonstrates that we require a very small amount of power from national grid if we can install dual axis solar tracker on rooftops of our residence; this is how increasing energy demand can effectively be met.