Han Su's research while affiliated with Jiangnan University and other places

In the letter, a stable magnetic tunnel junction (MTJ) at 300 K that is changed stochastically by spin Hall effect (SHE) and voltage controlled magnetic anisotropy (VCMA) is designed as a switching component of a neuron, which is used to implement restricted Boltzmann machine (RBM) as on-chip feature extractor. With the effect of VCMA, the naturally probability of the device can be regulated to be acted as the activation function of RBM. An online training scheme with VCMA-assisted SHE-MTJ and modified crossbar architecture is presented, which achieves high accuracy as well as neuron and synaptic sparsity. To meet the low power and low cost requirements, the training procedure and crossbar architecture is adjusted to be an offline training scheme, which achieves 72.50% energy consumption reduction of extraction and 2.84% accuracy promotion to 95.48%.

With the emerging of Internet of Things (IoT) industry, applications like smart power plugs, security ID tags, home automation and wearable electronic devices all make the demand for low-power WiFi chips impendency. In this paper, a low-power 2.4 GHz 802.11b/g/n WiFi system-on-chip (SoC) is designed and implemented with 40-nm CMOS process, with area of 8.1 mm². The low-power SoC integrates 32-bit microcontroller, 802.11b/g/n WiFi baseband, 2.4 GHz RF transceiver, ample memory space, ADC, 6-channel PWM, flexible I/O interfaces, multi-stage power management module, etc.. It has several sleep modes with extremely low leakage current as 0.8 mA/12 µA in light/deep sleep mode and 0.4 µA in Shutdown mode to reduce the power consumption. High performance is demonstrated, including Pout (-28 dB/-30 dB EVM) of 20.1 dBm/19.1 dBm and RX sensitivity of -76 dBm/-74 dBm meanwhile the total current of 148.5 mA/146.5 mA (TX) for 54 Mbps OFDM/HT20 MCS7.