Browsing by Author "Liu, Chun-Chen"

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A Reconfigurable Streaming Deep Convolutional Neural Network Accelerator for Internet of Things
(2018-01-01)
Du, Li
;
Du, Yuan
;
Li, Yilei
;
Su, Junjie
;
Kuan, Yen-Cheng
Convolutional neural network (CNN) offers significant accuracy in image detection. To implement image detection using CNN in the Internet of Things (IoT) devices, a streaming hardware accelerator is proposed. The proposed accelerator optimizes the energy efficiency by avoiding unnecessary data movement. With unique filter decomposition technique, the accelerator can support arbitrary convolution window size. In addition, max-pooling function can be computed in parallel with convolution by using separate pooling unit, thus achieving throughput improvement. A prototype accelerator was implemented in TSMC 65-nm technology with a core size of 5 mm(2). The accelerator can support major CNNs and achieve 152GOPS peak throughput and 434GOPS/W energy efficiency at 350 mW, making it a promising hardware accelerator for intelligent IoT devices.
A Single Layer 3-D Touch Sensing System for Mobile Devices Application
(2018-02-01)
Du, Li
;
Liu, Chun-Chen
;
Zhang, Yan
;
Li, Yilei
;
Du, Yuan
Touch sensing has been widely implemented as a main methodology to bridge human and machine interactions. The traditional touch sensing range is 2-D and therefore limits the user experience. To overcome these limitations, we propose a novel 3-D contactless touch sensing called Airtouch system, which improves user experience by remotely detecting single/multi-finger position. A single layer touch panel with triangle-shaped electrodes is proposed to achieve multitouch detection capability as well as manufacturing cost reduction. Moreover, an oscillator-based-capacitive touch sensing circuit is implemented as the sensing hardware with the bootstrapping technique to eliminate the interchannel coupling effects. To further improve the system accuracy, a grouping algorithm is proposed to group the useful channels' data and filter out hardware noise impact. Finally, improved algorithms are proposed to eliminate the fringing capacitance effect and achieve accurate finger position estimation. EM simulation proved that the proposed algorithm reduced the maximum systematic error by 11 dB in the horizontal position detection. The proposed system consumes 2.3 mW and is fully compatible with existing mobile device environments. A prototype is built to demonstrate that the system can successfully detect finger movement in a vertical direction up to 6 cm and achieve a horizontal resolution up to 0.6 cm at 1 cm finger-height. As a new interface for human and machine interactions, this system offers great potential in finger movement detection and gesture recognition for small-sized electronics and advanced human interactive games for mobile device.