An investigation into adaptive power reduction techniques for neural hardware

In light of the growing applicability of Artificial Neural Network (ANN) in the signal processing field [1] and the present thrust of the semiconductor industry towards lowpower SOCs for mobile devices [2], the power consumption of ANN hardware has become a very important implementation issue. Adaptability is a powerful and useful feature of neural networks. All current approaches for low-power ANN hardware techniques are ‘non-adaptive’ with respect to the power consumption of the network (i.e. power-reduction is not an objective of the adaptation/learning process). In the research work presented in this thesis, investigations on possible adaptive power reduction techniques have been carried out, which attempt to exploit the adaptability of neural networks in order to reduce the power consumption. Three separate approaches for such adaptive power reduction are proposed: adaptation of size, adaptation of network weights and adaptation of calculation precision. Initial case studies exhibit promising results with significantpower reduction

Memory and information processing in neuromorphic systems

A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system

A Decade of Neural Networks: Practical Applications and Prospects

The Jet Propulsion Laboratory Neural Network Workshop, sponsored by NASA and DOD, brings together sponsoring agencies, active researchers, and the user community to formulate a vision for the next decade of neural network research and application prospects. While the speed and computing power of microprocessors continue to grow at an ever-increasing pace, the demand to intelligently and adaptively deal with the complex, fuzzy, and often ill-defined world around us remains to a large extent unaddressed. Powerful, highly parallel computing paradigms such as neural networks promise to have a major impact in addressing these needs. Papers in the workshop proceedings highlight benefits of neural networks in real-world applications compared to conventional computing techniques. Topics include fault diagnosis, pattern recognition, and multiparameter optimization

Eine Test- und Ansteuerschaltung für eine neuartige 3D Verbindungstechnologie

In der vorliegenden Arbeit wird eine Built-In Self-Test Schaltung (BIST) vorgestellt, welche die vertikalen Inter-Chip-Verbindungen in einer neuartigen 3D Schaltungstechnologie auf ihre Funktionalität zur Datenübertragung überprüft. Die 3D Technologie beruht auf der Stapelung mehrerer aktiver Silizium-CMOS-ICs, welche durch das Siliziumsubstrat hindurch vertikal miteinander elektrisch verbunden sind. Bei diesen Vias sind die zu erwartenden Defekte hochohmige Verbindungen und Kurzschlüsse. </p><p style=&quot;line-height: 20px;&quot;> Die entwickelte Testschaltung ermöglicht es, beliebige Konstellationen von vertikalen Verbindungen auf Fehler zu untersuchen, und das Ergebnis entweder zur Analyse der 3D Technologie auszulesen oder innerhalb des Chipstapels zu verwenden, um defekte Vias zu umgehen. Die Schaltung wurde in einer 0,13μm Technologie entworfen und simuliert. Ein Testchip ist momentan in Produktion

Yield-improving test and routing circuits for a novel 3-D interconnect technology

This work presents a system to increase the yield of a novel 3-D chip integration technology. A built-in self-test and a routing system have been developed to identify and avoid faults on vertical connections between different stacked chips. The 3-D technology is based on stacking several active CMOS-ICs, which have through-substrate electrical contacts to communicate with each other. The expected defects of these vias are shorts and resistances that are too high. <P> The test and routing system is designed to analyze an arbitrary number of connections. The result ist used to gain information about the reliability of the new 3-D processing and to increase its yield. The circuits have been developed in 0.13 μm technology, one chip has been fabricated and tested, another one is in production

A Mixed-Signal Feed-Forward Neural Network Architecture Using A High-Resolution Multiplying D/A Conversion Method

Artificial Neural Networks (ANNs) are parallel processors capable of learning from a set of sample data using a specific learning rule. Such systems are commonly used in applications where human brain may surpass conventional computers such as image processing, speech/character recognition, intelligent control and robotics to name a few. In this thesis, a mixed-signal neural network architecture is proposed employs a high resolution Multiplying Digital to Analog Converter (MDAC) designed using Delta Sigma Modulation (DSM). To reduce chip are, multiplexing is used in addition to analog implementation of arithmetic operations. This work employs a new method for filtering the high bit-rate signals using neurons nonlinear transfer function already existing in the network. Therefore, a configuration of a few MOS transistors are replacing the large resistors required to implement the low-pass filter in the network. This configuration noticeably decreases the chip area and also makes multiplexing feasible for hardware implementation

Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world