Intelligent Embedded Software: New Perspectives and Challenges

Intelligent embedded systems (IES) represent a novel and promising generation of embedded systems (ES). IES have the capacity of reasoning about their external environments and adapt their behavior accordingly. Such systems are situated in the intersection of two different branches that are the embedded computing and the intelligent computing. On the other hand, intelligent embedded software (IESo) is becoming a large part of the engineering cost of intelligent embedded systems. IESo can include some artificial intelligence (AI)-based systems such as expert systems, neural networks and other sophisticated artificial intelligence (AI) models to guarantee some important characteristics such as self-learning, self-optimizing and self-repairing. Despite the widespread of such systems, some design challenging issues are arising. Designing a resource-constrained software and at the same time intelligent is not a trivial task especially in a real-time context. To deal with this dilemma, embedded system researchers have profited from the progress in semiconductor technology to develop specific hardware to support well AI models and render the integration of AI with the embedded world a reality

Doctor of Philosophy

dissertationSince the late 1950s, scientists have been working toward realizing implantable devices that would directly monitor or even control the human body's internal activities. Sophisticated microsystems are used to improve our understanding of internal biological processes in animals and humans. The diversity of biomedical research dictates that microsystems must be developed and customized specifically for each new application. For advanced long-term experiments, a custom designed system-on-chip (SoC) is usually necessary to meet desired specifications. Custom SoCs, however, are often prohibitively expensive, preventing many new ideas from being explored. In this work, we have identified a set of sensors that are frequently used in biomedical research and developed a single-chip integrated microsystem that offers the most commonly used sensor interfaces, high computational power, and which requires minimum external components to operate. Included peripherals can also drive chemical reactions by setting the appropriate voltages or currents across electrodes. The SoC is highly modular and well suited for prototyping in and ex vivo experimental devices. The system runs from a primary or secondary battery that can be recharged via two inductively coupled coils. The SoC includes a 16-bit microprocessor with 32 kB of on chip SRAM. The digital core consumes 350 μW at 10 MHz and is capable of running at frequencies up to 200 MHz. The integrated microsystem has been fabricated in a 65 nm CMOS technology and the silicon has been fully tested. Integrated peripherals include two sigma-delta analog-to-digital converters, two 10-bit digital-to-analog converters, and a sleep mode timer. The system also includes a wireless ultra-wideband (UWB) transmitter. The fullydigital transmitter implementation occupies 68 x 68 μm2 of silicon area, consumes 0.72 μW static power, and achieves an energy efficiency of 19 pJ/pulse at 200 MHz pulse repetition frequency. An investigation of the suitability of the UWB technology for neural recording systems is also presented. Experimental data capturing the UWB signal transmission through an animal head are presented and a statistical model for large-scale signal fading is developed

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

GSFC Annual Scan Technology Review SpaceCube On-Board Processor Update

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