Finding optimal solutions for Combinatorial Optimization (CO) problems is challenging. Compared to power-hungry cryogenic quantum computer and time-consuming classical computer, quantum-inspired Ising machine solves CO problems at room temperature with fast optimization speed, low power consumption, and low cost. Nevertheless, the Ising machine still faces several challenges: digital CMOS Ising machines increase interaction freedom at the cost of greater area and larger processing time; in analog Ising machine, oscillator spins find it hard to differentiate spin states without the assistance of the post-processing algorithm, and latch spins suffer from mismatches. To address these issues, we propose an oscillator-based 3-D CMOS Analog Ising Machine (CAIM) which adopts the 12-way toroidal Hexagonal Close Packed (HCP) structure, Supply-And-Bulk Injection Locking (SABIL), and dual-mode tunable coupler. The toroidal HCP structure exhibits >2 & times; interactions compared to conventional 3-D Ising machine, while SABIL and dual-mode tunable coupler settle oscillators to a bistable ground state 2.2 & times; quicker with an 8.5 & times; wider lock range (within 5 cycles). The proposed CAIM successfully solves 3-D max-cut problems and achieves a normalized Hamiltonian energy of more than 0.98 with a maximum perfect accuracy prevalence (PAP) of 91.67%. Measurement results on a sample random Max-Cut instance demonstrate that CAIM converges to within 1.7% of the reference optimum in 5 cycles.

Depression is a critical public health concern characterized by underdiagnosis, often due to stigma, lack of awareness, and reluctance to seek help. Cases of delayed intervention could be alleviated by wearable solutions which enable continuous and unobtrusive monitoring of depression indicators. Compared to electroencephalogram (EEG)-based and video-based depression recognition, speech-based approaches can be performed without deliberate user attention. However, due to the limited accuracy of existing algorithms and constrained resources at edge, performing accurate speech-based depression recognition on wearable platforms remains a challenge. Therefore, to achieve unobtrusive, accurate, and efficient depression recognition at edge, this work presents a hierarchical local–global network (HLG-Net) and optimized processor design for speech-based depression recognition. The proposed HLG-Net integrates convolutional neural networks (CNNs) with multihead attention (MHA) mechanism to simultaneously capture local acoustic features and global utterance-level coherence, enhancing depression stage recognition. For efficient processor design, a cross-layer buffered dataflow is proposed for efficient data handling, reducing data storage by 98.77%. The computing unit (CU) employs layer fusion, operator optimization, and quantization techniques to further improve resource utilization and reduce power consumption while preserving recognition accuracy. System-level low-power techniques such as clock/input gating and near-threshold design for application specific integrated circuit (ASIC) further reduce power consumption. The proposed processor implemented on field-programmable gate array (FPGA) (XC7Z100-2FFG900) achieves the lowest reported mean absolute error (MAE) of 5.13 on AVEC 2014 database. The 180-nm ASIC implementation shows a simulated power consumption of$17.4~\mu $W at 0.4 V. The results demonstrate the feasibility of accurate and efficient speech-based depression recognition on wearables.