A 1.02-??W STT-MRAM-Based DNN ECG arrhythmia monitoring SoC with leakage-based delay MAC unit

A low-power STT-MRAM-based mixed-mode electrocardiogram (ECG) arrhythmia monitoring SoC is proposed. The proposed SoC consists of 1-MB STT-MRAM, leakage-based delay multiply-and-accumulation (MAC) unit (LDMAC), and ECG analog front end (AFE). ResNet structure with 16 1-D convolution layers and max-pooling layers is adopted for the ECG arrhythmia detection with weight reusing and partial sum reusing scheme. A nonvolatile 1-MB STT-MRAM enables deep neural network (DNN) inference to achieve higher area efficiency, lower power consumption without external memory access. The proposed mixed-mode LDMAC consumes only 4.11-nW MAC power by reusing leakage current. The proposed SoC is fabricated in 28-nm FDSOI process with 7.29-mm2 area. It demonstrates ECG arrhythmia detection with 85.1% accuracy, which is the highest score reported, and the lowest power consumption of 1.02 ??W. ?? 2018 IEEE

A 502-GOPS and 0.984-mW Dual-Mode Intelligent ADAS SoC With Real-Time Semiglobal Matching and Intention Prediction for Smart Automotive Black Box System

The advanced driver assistance system (ADAS) for adaptive cruise control and collision avoidance is strongly dependent upon the robust image recognition technology such as lane detection, vehicle/pedestrian detection, and traffic sign recognition. However, the conventional ADAS cannot realize more advanced collision evasion in real environments due to the absence of intelligent vehicle/pedestrian behavior analysis. Moreover, accurate distance estimation is essential in ADAS applications and semiglobal matching (SGM) is most widely adopted for high accuracy, but its system-on-chip (SoC) implementation is difficult due to the massive external memory bandwidth. In this paper, an ADAS SoC with behavior analysis with Artificial Intelligence functions and hardware implementation of SGM is proposed. The proposed SoC has dual-mode operations of highperformance operation for intelligent ADAS with real-time SGM in D-Mode (d-mode) and ultralow-power operation for black box system in parking-mode. It features: 1) task-level pipelined SGM processor to reduce external memory bandwidth by 85.8%; 2) region-of-interest generation processor to reduce 86.2% of computation; 3) mixed-mode intention prediction engine for dualmode intelligence; and 4) dynamic voltage and frequency scaling control to save 36.2% of power in d-mode. The proposed ADAS processor achieves 862 GOPS/W energy efficiency and 31.4GOPS/ mm(2) area efficiency, which are 1.53x and 1.75x improvements than the state of the art, with 30 frames/s throughput under 720p stereo inputs

A 502GOPS and 0.984mW dual-mode ADAS SoC with RNN-FIS engine for intention prediction in automotive black-box system

Advanced driver-assistance systems (ADAS) are being adopted in automobiles for forward-collision warning, advanced emergency braking, adaptive cruise control, and lane-keeping assistance. Recently, automotive black boxes are installed in cars for tracking accidents or theft. In this paper, a dual-mode ADAS SoC is proposed to support both high-performance ADAS functionality in driving-mode (d-mode) and an ultra-low-power black box in parking-mode (p-mode). By operating in p-mode, surveillance recording can be triggered intelligently with the help of our intention-prediction engine (IPE), instead of always-on recording to extend battery life and prevent discharge

A 1.4-m Omega-Sensitivity 94-dB Dynamic-Range Electrical Impedance Tomography SoC and 48-Channel Hub-SoC for 3-D Lung Ventilation Monitoring System

A wearable electrical impedance tomography (EIT) system is proposed for the portable real-time 3-D lung ventilation monitoring. It consists of two types of SoCs, active electrode (AE)-SoC and Hub-SoC, mounted on wearable belts. The 48-channel AE-SoCs are integrated on flexible printed circuit board belt, and Hub-SoC is integrated in the hub module which performs data gathering and wireless communication between an external imaging device. To get high accuracy under the variation of conductivity, the dual-mode current stimulator provides the optimal frequency for time difference-EIT and frequency difference-EIT with simultaneous 4 k-128 kHz impedance sensing. A wide dynamic range instruments amplifier is proposed to provide 94 dB of wide dynamic range impedance sensing. In addition, the 48-channel AE system with the dedicated communication and calibration is implemented to achieve 1.4-m Omega sensitivity of impedance difference in the in vivo environment. The AE-/Hub-SoCs occupy 3.2 and 1.3 mm2in 65-nm CMOS technology and consume 124 mu W and 1.1 mW with 1.2 V supply, respectively. As a result, EIT images are reconstructed with 90% of accuracy, and up to 10 frames/s real-time 3-D lung images are successfully displayed