Mixed Signal Integrated Circuit Design for Custom Sensor Interfacing

Low-power analog integrated circuits (ICs) can be utilized at the interface between an analog sensor and a digital system\u27s input to decrease power consumption, increase system accuracy, perform signal processing, and make the necessary adjustments for compatibility between the two devices. This interfacing has typically been done with custom integrated solutions, but advancements in floating-gate technologies have made reconfigurable analog ICs a competitive option. Whether the solution is a custom design or built from a reconfigurable system, digital peripheral circuits are needed to configure their operation for these analog circuits to work with the best accuracy.;Using an analog IC as a front end signal processor between an analog sensor and wireless sensor mote can greatly decrease battery consumption. Processing in the digital domain requires more power than when done on an analog system. An Analog Signal Processor (ASP) can allow the digital wireless mote to remain in sleep mode while the ASP is always listening for an important event. Once this event occurs, the ASP will wake the wireless mote, allowing it to record the event and send radio transmissions if necessary. As most wireless sensor networks employ the use of batteries as a power source, an energy harvesting system in addition to an ASP can be used to further supplement this battery consumption.;This thesis documents the development of mixed-signal integrated circuits for use as interfaces between analog sensors and digital Wireless Sensor Networks (WSNs). The following work outlines, as well as shows the results, of development for sensor interfacing utilizing both custom mixed signal integrated circuits as well as a Field Programmable Analog Array (FPAA) for post fabrication customization. An Analog Signal Processor (ASP) has been used in an Acoustic Vehicle Classification system. To keep these interfacing methods low power, a prototype energy harvesting system using commercial-off-the-shelf (COTS) devices is detailed which has led to the design of a fully integrated solution

A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)

Neuromorphic computing systems comprise networks of neurons that use asynchronous events for both computation and communication. This type of representation offers several advantages in terms of bandwidth and power consumption in neuromorphic electronic systems. However, managing the traffic of asynchronous events in large scale systems is a daunting task, both in terms of circuit complexity and memory requirements. Here we present a novel routing methodology that employs both hierarchical and mesh routing strategies and combines heterogeneous memory structures for minimizing both memory requirements and latency, while maximizing programming flexibility to support a wide range of event-based neural network architectures, through parameter configuration. We validated the proposed scheme in a prototype multi-core neuromorphic processor chip that employs hybrid analog/digital circuits for emulating synapse and neuron dynamics together with asynchronous digital circuits for managing the address-event traffic. We present a theoretical analysis of the proposed connectivity scheme, describe the methods and circuits used to implement such scheme, and characterize the prototype chip. Finally, we demonstrate the use of the neuromorphic processor with a convolutional neural network for the real-time classification of visual symbols being flashed to a dynamic vision sensor (DVS) at high speed.Comment: 17 pages, 14 figure

Digital implementation of the cellular sensor-computers

Two different kinds of cellular sensor-processor architectures are used nowadays in various applications. The first is the traditional sensor-processor architecture, where the sensor and the processor arrays are mapped into each other. The second is the foveal architecture, in which a small active fovea is navigating in a large sensor array. This second architecture is introduced and compared here. Both of these architectures can be implemented with analog and digital processor arrays. The efficiency of the different implementation types, depending on the used CMOS technology, is analyzed. It turned out, that the finer the technology is, the better to use digital implementation rather than analog

Form Factor Improvement of Smart-Pixels for Vision Sensors through 3-D Vertically- Integrated Technologies

While conventional CMOS active pixel sensors embed only the circuitry required for photo-detection, pixel addressing and voltage buffering, smart pixels incorporate also circuitry for data processing, data storage and control of data interchange. This additional circuitry enables data processing be realized concurrently with the acquisition of images which is instrumental to reduce the number of data needed to carry to information contained into images. This way, more efficient vision systems can be built at the cost of larger pixel pitch. Vertically-integrated 3D technologies enable to keep the advnatges of smart pixels while improving the form factor of smart pixels.Office of Naval Research N000141110312Ministerio de Ciencia e Innovación IPT-2011-1625-43000

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

Can my chip behave like my brain?

Many decades ago, Carver Mead established the foundations of neuromorphic systems. Neuromorphic systems are analog circuits that emulate biology. These circuits utilize subthreshold dynamics of CMOS transistors to mimic the behavior of neurons. The objective is to not only simulate the human brain, but also to build useful applications using these bio-inspired circuits for ultra low power speech processing, image processing, and robotics. This can be achieved using reconfigurable hardware, like field programmable analog arrays (FPAAs), which enable configuring different applications on a cross platform system. As digital systems saturate in terms of power efficiency, this alternate approach has the potential to improve computational efficiency by approximately eight orders of magnitude. These systems, which include analog, digital, and neuromorphic elements combine to result in a very powerful reconfigurable processing machine.Ph.D

Programmable CMOS Analog-to-Digital Converter Design and Testability

In this work, a programmable second order oversampling CMOS delta-sigma analog-to-digital converter (ADC) design in 0.5µm n-well CMOS processes is presented for integration in sensor nodes for wireless sensor networks. The digital cascaded integrator comb (CIC) decimation filter is designed to operate at three different oversampling ratios of 16, 32 and 64 to give three different resolutions of 9, 12 and 14 bits, respectively which impact the power consumption of the sensor nodes. Since the major part of power consumed in the CIC decimator is by the integrators, an alternate design is introduced by inserting coder circuits and reusing the same integrators for different resolutions and oversampling ratios to reduce power consumption. The measured peak signal-to-noise ratio (SNR) for the designed second order delta-sigma modulator is 75.6dB at an oversampling ratio of 64, 62.3dB at an oversampling ratio of 32 and 45.3dB at an oversampling ratio of 16. The implementation of a built-in current sensor (BICS) which takes into account the increased background current of defect-free circuits and the effects of process variation on ΔIDDQ testing of CMOS data converters is also presented. The BICS uses frequency as the output for fault detection in CUT. A fault is detected when the output frequency deviates more than ±10% from the reference frequency. The output frequencies of the BICS for various model parameters are simulated to check for the effect of process variation on the frequency deviation. A design for on-chip testability of CMOS ADC by linear ramp histogram technique using synchronous counter as register in code detection unit (CDU) is also presented. A brief overview of the histogram technique, the formulae used to calculate the ADC parameters, the design implemented in 0.5µm n-well CMOS process, the results and effectiveness of the design are described. Registers in this design are replaced by 6T-SRAM cells and a hardware optimized on-chip testability of CMOS ADC by linear ramp histogram technique using 6T-SRAM as register in CDU is presented. The on-chip linear ramp histogram technique can be seamlessly combined with ΔIDDQ technique for improved testability, increased fault coverage and reliable operation