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

Analog signal processing on a reconfigurable platform

The Cooperative Analog/Digital Signal Processing (CADSP) research group's approach to signal processing is to see what opportunities lie in adjusting the line between what is traditionally computed in digital and what can be done in analog. By allowing more computation to be done in analog, we can take advantage of its low power, continuous domain operation, and parallel capabilities. One setback keeping Analog Signal Processing (ASP) from achieving more wide-spread use, however, is its lack of programmability. The design cycle for a typical analog system often involves several iterations of the fabrication step, which is labor intensive, time consuming, and expensive. These costs in both time and money reduce the likelihood that engineers will consider an analog solution. With CADSP's development of a reconfigurable analog platform, a Field-Programmable Analog Array (FPAA), it has become much more practical for systems to incorporate processing in the analog domain. In this Thesis, I present an entire chain of tools that allow one to design simply at the system block level and then compile that design onto analog hardware. This tool chain uses the Simulink design environment and a custom library of blocks to create analog systems. I also present several of these ASP blocks, covering a broad range of functions from matrix computation to interfacing. In addition to these tools and blocks, the most recent FPAA architectures are discussed. These include the latest RASP general-purpose FPAAs as well as an adapted version geared toward high-speed applications.M.S.Committee Chair: Hasler, Paul; Committee Member: Anderson, David; Committee Member: Ghovanloo, Maysa

Low-Power and Programmable Analog Circuitry for Wireless Sensors

Embedding networks of secure, wirelessly-connected sensors and actuators will help us to conscientiously manage our local and extended environments. One major challenge for this vision is to create networks of wireless sensor devices that provide maximal knowledge of their environment while using only the energy that is available within that environment. In this work, it is argued that the energy constraints in wireless sensor design are best addressed by incorporating analog signal processors. The low power-consumption of an analog signal processor allows persistent monitoring of multiple sensors while the device\u27s analog-to-digital converter, microcontroller, and transceiver are all in sleep mode. This dissertation describes the development of analog signal processing integrated circuits for wireless sensor networks. Specific technology problems that are addressed include reconfigurable processing architectures for low-power sensing applications, as well as the development of reprogrammable biasing for analog circuits

Low-Power and Programmable Analog Circuitry for Wireless Sensors

Embedding networks of secure, wirelessly-connected sensors and actuators will help us to conscientiously manage our local and extended environments. One major challenge for this vision is to create networks of wireless sensor devices that provide maximal knowledge of their environment while using only the energy that is available within that environment. In this work, it is argued that the energy constraints in wireless sensor design are best addressed by incorporating analog signal processors. The low power-consumption of an analog signal processor allows persistent monitoring of multiple sensors while the device\u27s analog-to-digital converter, microcontroller, and transceiver are all in sleep mode. This dissertation describes the development of analog signal processing integrated circuits for wireless sensor networks. Specific technology problems that are addressed include reconfigurable processing architectures for low-power sensing applications, as well as the development of reprogrammable biasing for analog circuits

Programming of Floating-Gate Transistors for Nonvolatile Analog Memory Array

Since they were introduced, floating-gate (FG) transistors have been used as non-volatile digital memory. Recent research has shown that floating-gate transistors can be successfully used as analog memory, specifically as programmable voltage and current sources. However, their proliferation has been limited due to the complex programming procedure and the complex testing equipment. Analog applications such as field-programmable analog arrays (FPAAs) require hundreds to thousands of floating-gate transistors on a single chip which makes the programming process even more complicated and very challenging. Therefore, a simplified, compact, and low-power scheme to program FGs are necessary. This work presents an improved version of the typical methodology for FG programming. Additionally, a novel programming methodology that utilizes negative voltages is presented here. This method simplifies the programming process by eliminating the use of supplementary and complicated infrastructure circuits, which makes the FG transistor a good candidate for low-power wireless sensor nodes and portable systems

Floating-Gate Design and Linearization for Reconfigurable Analog Signal Processing

Analog and mixed-signal integrated circuits have found a place in modern electronics design as a viable alternative to digital pre-processing. With metrics that boast high accuracy and low power consumption, analog pre-processing has opened the door to low-power state-monitoring systems when it is utilized in place of a power-hungry digital signal-processing stage. However, the complicated design process required by analog and mixed-signal systems has been a barrier to broader applications. The implementation of floating-gate transistors has begun to pave the way for a more reasonable approach to analog design. Floating-gate technology has widespread use in the digital domain. Analog and mixed-signal use of floating-gate transistors has only become a rising field of study in recent years. Analog floating gates allow for low-power implementation of mixed-signal systems, such as the field-programmable analog array, while simultaneously opening the door to complex signal-processing techniques. The field-programmable analog array, which leverages floating-gate technologies, is demonstrated as a reliable replacement to signal-processing tasks previously only solved by custom design. Living in an analog world demands the constant use and refinement of analog signal processing for the purpose of interfacing with digital systems. This work offers a comprehensive look at utilizing floating-gate transistors as the core element for analog signal-processing tasks. This work demonstrates the floating gate\u27s merit in large reconfigurable array-driven systems and in smaller-scale implementations, such as linearization techniques for oscillators and analog-to-digital converters. A study on analog floating-gate reliability is complemented with a temperature compensation scheme for implementing these systems in ever-changing, realistic environments

Configurable analog hardware for neuromorphic Bayesian inference and least-squares solutions

Sparse approximation is a Bayesian inference program with a wide number of signal processing applications, such as Compressed Sensing recovery used in medical imaging. Previous sparse coding implementations relied on digital algorithms whose power consumption and performance scale poorly with problem size, rendering them unsuitable for portable applications, and a bottleneck in high speed applications. A novel analog architecture, implementing the Locally Competitive Algorithm (LCA), was designed and programmed onto a Field Programmable Analog Arrays (FPAAs), using floating gate transistors to set the analog parameters. A network of 6 coefficients was demonstrated to converge to similar values as a digital sparse approximation algorithm, but with better power and performance scaling. A rate encoded spiking algorithm was then developed, which was shown to converge to similar values as the LCA. A second novel architecture was designed and programmed on an FPAA implementing the spiking version of the LCA with integrate and fire neurons. A network of 18 neurons converged on similar values as a digital sparse approximation algorithm, with even better performance and power efficiency than the non-spiking network. Novel algorithms were created to increase floating gate programming speed by more than two orders of magnitude, and reduce programming error from device mismatch. A new FPAA chip was designed and tested which allowed for rapid interfacing and additional improvements in accuracy. Finally, a neuromorphic chip was designed, containing 400 integrate and fire neurons, and capable of converging on a sparse approximation solution in 10 microseconds, over 1000 times faster than the best digital solution.Ph.D

Analog Computing using 1T1R Crossbar Arrays

Memristor is a novel passive electronic device and a promising candidate for new generation non-volatile memory and analog computing. Analog computing based on memristors has been explored in this study. Due to the lack of commercial electrical testing instruments for those emerging devices and crossbar arrays, we have designed and built testing circuits to implement analog and parallel computing operations. With the setup developed in this study, we have successfully demonstrated image processing functions utilizing large memristor crossbar arrays. We further designed and experimentally demonstrated the first memristor based field programmable analog array (FPAA), which was successfully configured for audio equalizer and frequency classifier demonstration as exemplary applications of such memristive FPAA (memFPAA)

Controller implementation using analog reconfigurable hardware (FPAA)

This Thesis has the main target to make a research about FPAA/dpASPs devices and technologies applied to control systems. These devices provide easy way to emulate analog circuits that can be reconfigurable by programming tools from manufactures and in case of dpASPs are able to be dynamically reconfigurable on the fly. It is described different kinds of technologies commercially available and also academic projects from researcher groups. These technologies are very recent and are in ramp up development to achieve a level of flexibility and integration to penetrate more easily the market. As occurs with CPLD/FPGAs, the FPAA/dpASPs technologies have the target to increase the productivity, reducing the development time and make easier future hardware reconfigurations reducing the costs. FPAA/dpAsps still have some limitations comparing with the classic analog circuits due to lower working frequencies and emulation of complex circuits that require more components inside the integrated circuit. However, they have great advantages in sensor signal condition, filter circuits and control systems. This thesis focuses practical implementations of these technologies to control system PID controllers. The result of the experiments confirms the efficacy of FPAA/dpASPs on signal condition and control systems.Esta tese tem como principal objectivo fazer uma pesquisa sobre circuitos integrados e tecnologias das FPAA/dpASPs aplicadas a sistemas de controlo. Estes dispositivos possibilitam a emulação de circuitos analógicos que podem ser reconfiguráveis por ferramentas de programação dos próprios fabricantes e no caso dos dpASPs são capazes de ser dinamicamente reconfiguráveis em tempo real. São descritas diferentes tecnologias disponíveis no mercado e também projectos académicos de grupos de investigação. Estas tecnologias são muito recentes e estão em pleno desenvolvimento para alcançar um nível de flexibilidade e integração para penetrar mais facilmente no mercado. Como já ocorre com as CPLD/FPGAs, os FPAA/dpASPs tem o objectivo de aumentar a produtividade, reduzindo o tempo de desenvolvimento e facilitar reconfigurações futuras de hardware, reduzindo os custos. As FPAA/dpASPs ainda tem algumas limitações comparando com os circuitos analógicos clássicos devido a uma menor largura de banda de frequências de trabalho e à dificuldade de emulação de circuitos complexos que requerem mais componentes dentro do circuito integrado e portanto uma maior escala de integração. No entanto, estes circuitos integrados têm grandes vantagens e podem ser utilizados para aplicações de condicionamento do sinal de sensores, circuitos de filtros e sistemas de controlo. Esta tese concentra-se nas implementações práticas destas tecnologias aos sistemas de controlo usando controladores PID. Os resultados das experiências confirmam a eficácia das FPAA/dpASPs no condicionamento de sinal e sistemas de controlo