ACE16K: A 128×128 focal plane analog processor with digital I/O

This paper presents a new generation 128×128 focal-plane analog programmable array processor (FPAPAP), from a system level perspective, which has been manufactured in a 0.35 μm standard digital 1P-5M CMOS technology. The chip has been designed to achieve the high-speed and moderate-accuracy (8b) requirements of most real time early-vision processing applications. It is easily embedded in conventional digital hosting systems: external data interchange and control are completely digital. The chip contains close to four millions transistors, 90% of them working in analog mode, and exhibits a relatively low power consumption-<4 W, i.e. less than 1 μW per transistor. Computing vs. power peak values are in the order of 1 TeraOPS/W, while maintained VGA processing throughputs of 100 frames/s are possible with about 10-20 basic image processing tasks on each frame

CMOS Architectures and circuits for high-speed decision-making from image flows

We present architectures, CMOS circuits and CMOS chips to process image flows at very high speed. This is achieved by exploiting bio-inspiration and performing processing tasks in parallel manner and concurrently with image acquisition. A vision system is presented which makes decisions within sub-msec range. This is very well suited for defense and security applications requiring segmentation and tracking of rapidly moving objects

A Modular Programmable CMOS Analog Fuzzy Controller Chip

We present a highly modular fuzzy inference analog CMOS chip architecture with on-chip digital programmability. This chip consists of the interconnection of parameterized instances of two different kind of blocks, namely label blocks and rule blocks. The architecture realizes a lattice partition of the universe of discourse, which at the hardware level means that the fuzzy labels associated to every input (realized by the label blocks) are shared among the rule blocks. This reduces the area and power consumption and is the key point for chip modularity. The proposed architecture is demonstrated through a 16-rule two input CMOS 1-μm prototype which features an operation speed of 2.5 Mflips (2.5×10^6 fuzzy inferences per second) with 8.6 mW power consumption. Core area occupation of this prototype is of only 1.6 mm 2 including the digital control and memory circuitry used for programmability. Because of the architecture modularity the number of inputs and rules can be increased with any hardly design effort.This work was supported in part by the Spanish C.I.C.Y.T under Contract TIC96-1392-C02- 02 (SIVA)

CMOS Vision Sensors: Embedding Computer Vision at Imaging Front-Ends

CMOS Image Sensors (CIS) are key for imaging technol-ogies. These chips are conceived for capturing opticalscenes focused on their surface, and for delivering elec-trical images, commonly in digital format. CISs may incor-porate intelligence; however, their smartness basicallyconcerns calibration, error correction and other similartasks. The term CVISs (CMOS VIsion Sensors) definesother class of sensor front-ends which are aimed at per-forming vision tasks right at the focal plane. They havebeen running under names such as computational imagesensors, vision sensors and silicon retinas, among others. CVIS and CISs are similar regarding physical imple-mentation. However, while inputs of both CIS and CVISare images captured by photo-sensors placed at thefocal-plane, CVISs primary outputs may not be imagesbut either image features or even decisions based on thespatial-temporal analysis of the scenes. We may hencestate that CVISs are more “intelligent” than CISs as theyfocus on information instead of on raw data. Actually,CVIS architectures capable of extracting and interpretingthe information contained in images, and prompting reac-tion commands thereof, have been explored for years inacademia, and industrial applications are recently ramp-ing up.One of the challenges of CVISs architects is incorporat-ing computer vision concepts into the design flow. Theendeavor is ambitious because imaging and computervision communities are rather disjoint groups talking dif-ferent languages. The Cellular Nonlinear Network Univer-sal Machine (CNNUM) paradigm, proposed by Profs.Chua and Roska, defined an adequate framework forsuch conciliation as it is particularly well suited for hard-ware-software co-design [1]-[4]. This paper overviewsCVISs chips that were conceived and prototyped at IMSEVision Lab over the past twenty years. Some of them fitthe CNNUM paradigm while others are tangential to it. Allthem employ per-pixel mixed-signal processing circuitryto achieve sensor-processing concurrency in the quest offast operation with reduced energy budget.Junta de Andalucía TIC 2012-2338Ministerio de Economía y Competitividad TEC 2015-66878-C3-1-R y TEC 2015-66878-C3-3-

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 mixed-signal early vision chip with embedded image and programming memories and digital I/O

From a system level perspective, this paper presents a 128 × 128 flexible and reconfigurable Focal-Plane Analog Programmable Array Processor, which has been designed as a single chip in a 0.35μm standard digital 1P-5M CMOS technology. The core processing array has been designed to achieve high-speed of operation and large-enough accuracy (∼ 7bit) with low power consumption. The chip includes on-chip program memory to allow for the execution of complex, sequential and/or bifurcation flow image processing algorithms. It also includes the structures and circuits needed to guarantee its embedding into conventional digital hosting systems: external data interchange and control are completely digital. The chip contains close to four million transistors, 90% of them working in analog mode. The chip features up to 330GOPs (Giga Operations per second), and uses the power supply (180GOP/Joule) and the silicon area (3.8 GOPS/mm2) efficiently, as it is able to maintain VGA processing throughputs of 100Frames/s with about 15 basic image processing tasks on each frame

Baseband analog front-end and digital back-end for reconfigurable multi-standard terminals

Multimedia applications are driving wireless network operators to add high-speed data services such as Edge (E-GPRS), WCDMA (UMTS) and WLAN (IEEE 802.11a,b,g) to the existing GSM network. This creates the need for multi-mode cellular handsets that support a wide range of communication standards, each with a different RF frequency, signal bandwidth, modulation scheme etc. This in turn generates several design challenges for the analog and digital building blocks of the physical layer. In addition to the above-mentioned protocols, mobile devices often include Bluetooth, GPS, FM-radio and TV services that can work concurrently with data and voice communication. Multi-mode, multi-band, and multi-standard mobile terminals must satisfy all these different requirements. Sharing and/or switching transceiver building blocks in these handsets is mandatory in order to extend battery life and/or reduce cost. Only adaptive circuits that are able to reconfigure themselves within the handover time can meet the design requirements of a single receiver or transmitter covering all the different standards while ensuring seamless inter-interoperability. This paper presents analog and digital base-band circuits that are able to support GSM (with Edge), WCDMA (UMTS), WLAN and Bluetooth using reconfigurable building blocks. The blocks can trade off power consumption for performance on the fly, depending on the standard to be supported and the required QoS (Quality of Service) leve

A versatile sensor interface for programmable vision systems-on-chip

This paper describes an optical sensor interface designed for a programmable mixed-signal vision chip. This chip has been designed and manufactured in a standard 0.35μm n-well CMOS technology with one poly layer and five metal layers. It contains a digital shell for control and data interchange, and a central array of 128 × 128 identical cells, each cell corresponding to a pixel. Die size is 11.885 × 12.230mm2 and cell size is 75.7μm × 73.3μm. Each cell contains 198 transistors dedicated to functions like processing, storage, and sensing. The system is oriented to real-time, single-chip image acquisition and processing. Since each pixel performs the basic functions of sensing, processing and storage, data transferences are fully parallel (image-wide). The programmability of the processing functions enables the realization of complex image processing functions based on the sequential application of simpler operations. This paper provides a general overview of the system architecture and functionality, with special emphasis on the optical interface.European Commission IST-1999-19007Office of Naval Research (USA) N00014021088