低功耗

These stimuli-switchable nanoparticles coated with the cleavable outer polymer coatingmultifunctional peptides may afford a new platform for anticancer drugmiR. These nanoparticles possess characteristics of environmental response, active targeting,gene/chemo combinatorial therapeutics, thus providing spatiotemporal control of posttranｓｃｒｉｐｔional gene regulation, smart nuclear/mitochondrial localization,simultaneous inhibition of multiple progression pathways of tumor cells for enhancing antitumor efficacysafety of cancer therapy.

Inorganic porous nanofibers with surfaceinterface defects are prepared through humidity-controlled electrospinninghigh-temperature annealing technology. Under the irradiation of light sources of different wavelengths (380~780 nm), the bound electrons stored in the valence band can be excited to the conduction band to form free electrons on the surface of the material, generating different intensities of microcurrents, light sensitivitymicrocurrent changes. Because the "inorganic nanofiber" technology has high uniquenesshigh product compatibility, it can be applied to a wide range of markets.

Inorganic porous nanofibers with surfaceinterface defects are prepared through humidity-controlled electrospinninghigh-temperature annealing technology. Under the irradiation of light sources of different wavelengths (380~780 nm), the bound electrons stored in the valence band can be excited to the conduction band to form free electrons on the surface of the material, generating different intensities of microcurrents, light sensitivitymicrocurrent changes. Because the "inorganic nanofiber" technology has high uniquenesshigh product compatibility, it can be applied to a wide range of markets.

This work presents a low powersecure IoT communication scheme. A physical unclonable function (PUF) based mutual authentication scheme in conjunction with a trusted third party (TTP),the low power transceiver with secure wake-up mechanism are integrated for networkhardware security thereby enabling to extend the battery lifetimeensuring reliability for the IoT systems.

"We will demonstrate the following three technical achievements of our joint project: 1. Deployment of HarDNet on GPU (power consumption: 200 Watts) 2. Deployment of HarDNet on FPGA (power consumption: several tens of Watts) [winning 2nd place in the FPGA track, LPCVC 2020] 3. Deployment of HarDNet on lightweight edge devices such as Raspberry Pi (power consumption: single-digit, 10 Watts) [winning 3rd place in the DSP track4th place in the CPU track, LPCVC 2020]"

A technique compatible to IC process has been presented to prepare gas sensing chips the lightly-doped region of nanoelectronic devices are grown with different sensing materials to form a gas sensor array. During gas sensing, individual nanodevice was Joule-heated, reducing power consumption to microWatts/device, solving the current high power consumption problem. With self-calibration of temperature, humidityinterfering gas, problems like cross-sensitivity, quantification, specificitysensitivity are solved. A demonstration of I+Nose with smart phone was presented for CO detection.

1.Low-Power Processing-in-sensor CMOS Image Sensor 2.Implementation of Low-PowerLow-Latency Deep Learning Chip based on Neuromorphic Intelligence (collaborated with MOST 108-2622-8-007-009) 3.Development of Neuromorphic Chip based on The Fruit Fly VisualSpatial Sensory Systems 4.Hardware-Software Co-Design for Neuromorphic AI Chips

Oscillator is a very important device of significant components in electronic products. This invent proposes a new method for calibrating oscillator frequency. By this technique, the oscillator product using this invent can output the frequency of clock signal flexibly from 1MHz to 200MHz through a programmer setting. This invent is the first low-cost oscillator control chip in the world that can set the frequency of output clock signal. In addition, this invent developed the smallest package 20x16 mm2 oscillator for next-generation consumer electronics in the world.

Low-power deep learning accelerator integrates neural network design / training, model compressionaccelerator design. It uses a small amount of computationmemory footprint to realize high performance on the edge device. The image semantic segmentation technology can reach 80 frames per second (resolution:1024*2048) to meet real-time requirements for autonomous driving.

Based on the mass production technique to fabricate 2D materials developed by our group, efficient gas sensors against NOx with sensitivity of 100 ppb are successfully demonstrated. In comparison to international competitors, our products exhibit superior sensitivityselectivity. Furthermore, the technique would be extended to other 2D materials to fabricate various gas sensors against multiple target gases. The integration with internet of things would then open new feasibilities for early monitoring in environmental pollutionhuman safety.

"The team established material doping technology to greatly improve the piezoelectric properties of lead-free materials,successfully prepared MEMS lead-free triaxial piezoelectric accelerometers. The use of lead-free materials is the world＇s first development result. In addition, the team also integrated sensors, back-end circuitsuser interfaces, applied the acceleration gauge system to the safety monitoring of unmanned vehicles,successfully integrated the intelligent integrated sensingcontrol system required in the Internet of Things era."

Dynamic vision sensors have been investigated to report motion-only images for moving object recognition. Less but essential information helps post-process recognition algorithm reduces computationimproves accuracy. Implement low powerlow latency deep Learning chip based on neuromorphic Intelligence. The neuromorphic obstacle detection algorithm integrates visualproprioceptive signals. The algorithm is characterized by its efficiencylow power consumption. We possess the next-generation in-memory computing AI chipsnext-generation UAV key softwarehardware technology