A Model for VLSI implementation of CNN image processing chips using current-mode techniques

A new Cellular Neural Network model is proposed which allows simpler and faster VLSI implementation than previous models. Current-mode building blocks are presented for the design of CMOS image preprocessing chips (feature extraction, noise filtering , compound component detection, etc.) using the cellular neural network paradigm. Area evaluation for the new model shows a reduction off about 50% as compared to the use of current-mode techniques with conventional models. Experimental measurements of CMOS prototypes designed in a 1.6 μm n-well double-metal single-poly technology are reported

SIRENA: A CAD environment for behavioural modelling and simulation of VLSI cellular neural network chips

This paper presents SIRENA, a CAD environment for the simulation and modelling of mixed-signal VLSI parallel processing chips based on cellular neural networks. SIRENA includes capabilities for: (a) the description of nominal and non-ideal operation of CNN analogue circuitry at the behavioural level; (b) performing realistic simulations of the transient evolution of physical CNNs including deviations due to second-order effects of the hardware; and, (c) evaluating sensitivity figures, and realize noise and Monte Carlo simulations in the time domain. These capabilities portray SIRENA as better suited for CNN chip development than algorithmic simulation packages (such as OpenSimulator, Sesame) or conventional neural networks simulators (RCS, GENESIS, SFINX), which are not oriented to the evaluation of hardware non-idealities. As compared to conventional electrical simulators (such as HSPICE or ELDO-FAS), SIRENA provides easier modelling of the hardware parasitics, a significant reduction in computation time, and similar accuracy levels. Consequently, iteration during the design procedure becomes possible, supporting decision making regarding design strategies and dimensioning. SIRENA has been developed using object-oriented programming techniques in C, and currently runs under the UNIX operating system and X-Windows framework. It employs a dedicated high-level hardware description language: DECEL, fitted to the description of non-idealities arising in CNN hardware. This language has been developed aiming generality, in the sense of making no restrictions on the network models that can be implemented. SIRENA is highly modular and composed of independent tools. This simplifies future expansions and improvements.Comisión Interministerial de Ciencia y Tecnología TIC96-1392-C02-0

Smart-Pixel Cellular Neural Networks in Analog Current-Mode CMOS Technology

This paper presents a systematic approach to design CMOS chips with concurrent picture acquisition and processing capabilities. These chips consist of regular arrangements of elementary units, called smart pixels. Light detection is made with vertical CMOS-BJT’s connected in a Darlington structure. Pixel smartness is achieved by exploiting the Cellular Neural Network paradigm [1], [2], incorporating at each pixel location an analog computing cell which interacts with those of nearby pixels. We propose a current-mode implementation technique and give measurements from two 16 x 16 prototypes in a single-poly double-metal CMOS n-well 1.6-µm technology. In addition to the sensory and processing circuitry, both chips incorporate light-adaptation circuitry for automatic contrast adjustment. They obtain smart-pixel densities up to 89 units/mm2, with a power consumption down to 105 µW/unit and image processing times below 2 µs

Current-Mode Techniques for the Implementation of Continuous- and Discrete-Time Cellular Neural Networks

This paper presents a unified, comprehensive approach to the design of continuous-time (CT) and discrete-time (DT) cellular neural networks (CNN) using CMOS current-mode analog techniques. The net input signals are currents instead of voltages as presented in previous approaches, thus avoiding the need for current-to-voltage dedicated interfaces in image processing tasks with photosensor devices. Outputs may be either currents or voltages. Cell design relies on exploitation of current mirror properties for the efficient implementation of both linear and nonlinear analog operators. These cells are simpler and easier to design than those found in previously reported CT and DT-CNN devices. Basic design issues are covered, together with discussions on the influence of nonidealities and advanced circuit design issues as well as design for manufacturability considerations associated with statistical analysis. Three prototypes have been designed for l.6-pm n-well CMOS technologies. One is discrete-time and can be reconfigured via local logic for noise removal, feature extraction (borders and edges), shadow detection, hole filling, and connected component detection (CCD) on a rectangular grid with unity neighborhood radius. The other two prototypes are continuous-time and fixed template: one for CCD and other for noise removal. Experimental results are given illustrating performance of these prototypes

An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics

Near-sensor data analytics is a promising direction for IoT endpoints, as it minimizes energy spent on communication and reduces network load - but it also poses security concerns, as valuable data is stored or sent over the network at various stages of the analytics pipeline. Using encryption to protect sensitive data at the boundary of the on-chip analytics engine is a way to address data security issues. To cope with the combined workload of analytics and encryption in a tight power envelope, we propose Fulmine, a System-on-Chip based on a tightly-coupled multi-core cluster augmented with specialized blocks for compute-intensive data processing and encryption functions, supporting software programmability for regular computing tasks. The Fulmine SoC, fabricated in 65nm technology, consumes less than 20mW on average at 0.8V achieving an efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to 25MIPS/mW in software. As a strong argument for real-life flexible application of our platform, we show experimental results for three secure analytics use cases: secure autonomous aerial surveillance with a state-of-the-art deep CNN consuming 3.16pJ per equivalent RISC op; local CNN-based face detection with secured remote recognition in 5.74pJ/op; and seizure detection with encrypted data collection from EEG within 12.7pJ/op.Comment: 15 pages, 12 figures, accepted for publication to the IEEE Transactions on Circuits and Systems - I: Regular Paper

From neural-based object recognition toward microelectronic eyes

Engineering neural network systems are best known for their abilities to adapt to the changing characteristics of the surrounding environment by adjusting system parameter values during the learning process. Rapid advances in analog current-mode design techniques have made possible the implementation of major neural network functions in custom VLSI chips. An electrically programmable analog synapse cell with large dynamic range can be realized in a compact silicon area. New designs of the synapse cells, neurons, and analog processor are presented. A synapse cell based on Gilbert multiplier structure can perform the linear multiplication for back-propagation networks. A double differential-pair synapse cell can perform the Gaussian function for radial-basis network. The synapse cells can be biased in the strong inversion region for high-speed operation or biased in the subthreshold region for low-power operation. The voltage gain of the sigmoid-function neurons is externally adjustable which greatly facilitates the search of optimal solutions in certain networks. Various building blocks can be intelligently connected to form useful industrial applications. Efficient data communication is a key system-level design issue for large-scale networks. We also present analog neural processors based on perceptron architecture and Hopfield network for communication applications. Biologically inspired neural networks have played an important role towards the creation of powerful intelligent machines. Accuracy, limitations, and prospects of analog current-mode design of the biologically inspired vision processing chips and cellular neural network chips are key design issues

Neuro-memristive Circuits for Edge Computing: A review

The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing

A one-transistor-synapse strategy for electrically-programmable massively-parallel analog array processors

This paper presents a linear, four-quadrants, electrically-programmable, one-transistor synapse strategy applicable to the implementation of general massively-parallel analog processors in CMOS technology. It is specially suited for translationally-invariant processing arrays with local connectivity, and results in a significant reduction in area occupation and power dissipation of the basic processing units. This allows higher integration densities and therefore, permits the integration of larger arrays on a single chip.Comisión Interministerial de Ciencia y Tecnología TIC96- 1392-C02-0

Low-power Design of a Neuromorphic IC and MICS Transceiver

abstract: The first part describes Metal Semiconductor Field Effect Transistor (MESFET) based fundamental analog building blocks designed and fabricated in a single poly, 3-layer metal digital CMOS technology utilizing fully depletion mode MESFET devices. DC characteristics were measured by varying the power supply from 2.5V to 5.5V. The measured DC transfer curves of amplifiers show good agreement with the simulated ones with extracted models from the same process. The accuracy of the current mirror showing inverse operation is within ±15% for the current from 0 to 1.5mA with the power supply from 2.5 to 5.5V. The second part presents a low-power image recognition system with a novel MESFET device fabricated on a CMOS substrate. An analog image recognition system with power consumption of 2.4mW/cell and a response time of 6µs is designed, fabricated and characterized. The experimental results verified the accuracy of the extracted SPICE model of SOS MESFETs. The response times of 4µs and 6µs for one by four and one by eight arrays, respectively, are achieved with the line recognition. Each core cell for both arrays consumes only 2.4mW. The last part presents a CMOS low-power transceiver in MICS band is presented. The LNA core has an integrated mixer in a folded configuration. The baseband strip consists of a pseudo differential MOS-C band-pass filter achieving demodulation of 150kHz-offset BFSK signals. The SRO is used in a wakeup RX for the wake-up signal reception. The all digital frequency-locked loop drives a class AB power amplifier in a transmitter. The sensitivity of -85dBm in the wakeup RX is achieved with the power consumption of 320µW and 400µW at the data rates of 100kb/s and 200kb/s from 1.8V, respectively. The sensitivities of -70dBm and -98dBm in the data-link RX are achieved with NF of 40dB and 11dB at the data rate of 100kb/s while consuming only 600µW and 1.5mW at 1.2V and 1.8V, respectively.Dissertation/ThesisPh.D. Electrical Engineering 201