Neuromorphic computing is one of the most promising technologies to solve the von Neumann bottleneck, which has the advantages of fast processing speed and low energy consumption in performing complex tasks. The development of neuromorphic computing is currently driven by several kinds of novel devices. Magnetic tunnel junctions (MTJs) are rich in nonlinear properties and can be regulated by multiple physical fields such as magnetic field, current and temperature. Meanwhile, MTJ has the advantages of good stability and low power consumption, which makes it an ideal device for neuromorphic computing. This paper starts by examining individual MTJ devices and then extends the discussion to full neural networks. First of all, we sorted out the various properties of MTJ, from the structure to physical mechanism and response characteristics. Secondly, the biological neuron model, synaptic properties and related studies on simulating neurons and synapses based on MTJs are introduced. Then, we review the neural network system-level architectures that have been explored with MTJ devices. Finally, the challenges and the future development trend are summarized for advancing MTJ-enabled neuromorphic computing.

Diabetes mellitus represents a major public health concern worldwide, with an estimated prevalence of approximately 9.1% of the population in Europe imposing a substantial economic burden on healthcare systems. One of the main complications of diabetes is the presence of Diabetic Foot Ulcers (DFUs) arising primarily from neuropathic and vascular impairments. Recent advances in sensing devices and photonics have stimulated the launch of sensors and imaging modules that can early diagnosis and prevent DFUs. In this paper we present the results of a new innovative, reliable, and cost-effective photonic-based system for the monitoring and management of DFUs, designed for large-scale clinical and home use. The system, developed within the framework of the H2020 PHOOTONICS project, integrates passive infrared photodetectors with active illuminators to achieve enhanced diagnostic capability. In PHOOTONICS project, two new photonic technologies for the early diagnosis and the management of diabetic foot have been developed; The professional device, called PRO and the home device called, HOME. The PRO version is dedicated for physicians at their offices or at hospitals. In this article, we present the clinical validation results of the new photonic-based device for diabetic foot ulcer. The results have been validated across four hospitals; ATTIKON University Hospital (Greece), VBMS University Hospital (Romania), CHARITE University Hospital (Germany), LEIDEN University Hospital (Netherlands). The validation includes (i) requirement process of diabetic and non-diabetic patients, patients stratification procedures, existence of comorbidities and an AI-based assessment of the results. For the latter we utilize ResNet deep models.

This paper presents a Weak Field-Programmable Gate Array Physically Unclonable Function (FPGA PUF) using Multiple Parallel Single Delay-Line based Time-to-Digital Converter (MPSDL-TDC) on FPGA. In the measurement mode, the proposed PUF works as a high-resolution FPGA TDC with the MPSDL-TDCs. In the PUF mode, the proposed PUF selects a stage of a TDC in the MPSDL-TDC and another stage of another TDC in it. Next, we calculate the buffer delays of the selected stages with the linearity self-calibration. Finally, we obtain a 1-bit response output by comparing the buffer delays. With a small amount of circuit, the proposed PUF can be applied to any type of MPSDL-TDCs, even it is the one with Look-Up Table (LUT) chain based TDCs or with the dedicated carry-chains based TDCs. Consequently, the number of the extra resources for the proposed PUF is small. Evaluation using 10 Artix-7 FPGAs resulted in an inter-chip Hamming distance of 47.07%, a reliability of 93.91%, and a uniformity of 50.70%. Minimum resource overhead for the proposed PUF over original MDSDL-TDCs is 4.99%.