Recent advances in neuromorphic computing demonstrate on-device learning capabilities with low power consumption. One of the key learning units in these systems is the winner-take-all circuit. In this research, we propose a winner-take-all circuit that can be configured to achieve k-winner and hysteresis properties, simulated in IBM 65 nm node. The circuit dissipated 34.9 $μ$W of power with a latency of 10.4 ns, while processing 1000 inputs. The utility of the circuit is demonstrated for spatial filtering and classification.

Modern information technology (IT) relies on division of labour. Photons carry data around the world and electrons process them. But, before optical fibres, electrons did both--and some people hope to complete the transition by having photons process data as well as carrying them. Your browser does not support the audio element. Unlike electrons, photons (which are electrically neutral) can cross each others' paths without interacting, so glass fibres can handle many simultaneous signals in a way that copper wires cannot.

Neuromorphic photonics/electronics is the future of ultralow energy intelligent computing and artificial intelligence (AI). In recent years, inspired by the human brain, artificial neuromorphic devices have attracted extensive attention, especially in simulating visual perception and memory storage. Because of its advantages of high bandwidth, high interference immunity, ultrafast signal transmission and lower energy consumption, neuromorphic photonic devices are expected to realize real-time response to input data. In addition, photonic synapses can realize non-contact writing strategy, which contributes to the development of wireless communication. The use of low-dimensional materials provides an opportunity to develop complex brain-like systems and low-power memory logic computers.

The chip designer Ambarella has announced two new chips for automotive cameras and advanced driver assistance systems (ADAS) based on its CVflow architecture for artificial intelligence processing. The Santa Clara, California-based company introduced the CV22FS and CV2FS automotive camera (SoC) systems with CVflow AI processing and ASIL-B compliance to enable critical safety applications. Ambarella will also demonstrate applications with its existing chips, as well as a robotic platform and Amazon SageMaker Neo technology to train machine learning models, at CES 2020, the big technology fair in Las Vegas this week. The company, which was made public in 2011, started as a manufacturer of low-power chips for video cameras. But he turned that ability into computer vision experience and launched his CVflow architecture in 2018 to create low-power artificial intelligence chips.

Scalable Deep Learning services are contingent on several constraints. Depending on your target application, you may require low latency, enhanced security or long-term cost effectiveness. Hosting your Deep Learning model on the cloud may not be the best solution in such cases. Deep Learning on the edge alleviates the above issues, and provides other benefits. Edge here refers to the computation that is performed locally on the consumer's products.

Companies around the world recently have become desperate to try and take advantage of Artificial intelligence (AI), as it is one of the most emerging and competitive technologies. However, a lot of AI technologies focus on the software, with operating speeds low which makes them a poor fit for mobile devices. So now, big companies are focusing on developing AI with low power and high speeds, hoping to make AI fit for mobile use. Professor Hoi-Jun Yoo of the Department of Electrical Engineering, along with his research team and collaboration with start-up company, UX Factory Co, has developed a semiconductor chip, CNNP (CNN Processor), which runs AI algorithms with ultra-low power, and K-Eye, a face recognition system using CNNP. Consisting of two different formats, the K-Eye series is available as a wearable type and a dongle type.

A research team led by Professor Hoi-Jun Yoo of the Department of Electrical Engineering has developed a semiconductor chip, CNNP (CNN Processor), that runs AI algorithms with ultra-low power, and K-Eye, a face recognition system using CNNP. The system was made in collaboration with a start-up company, UX Factory Co. The K-Eye series consists of two types: a wearable type and a dongle type. The wearable type device can be used with a smartphone via Bluetooth, and it can operate for more than 24 hours with its internal battery. Users hanging K-Eye around their necks can conveniently check information about people by using their smartphone or smart watch, which connects K-Eye and allows users to access a database via their smart devices.