Neural networks-on-chip for hybrid bio-electronic systems

PhD ThesisBy modelling the brains computation we can further our understanding of its function and develop novel treatments for neurological disorders. The brain is incredibly powerful and energy e cient, but its computation does not t well with the traditional computer architecture developed over the previous 70 years. Therefore, there is growing research focus in developing alternative computing technologies to enhance our neural modelling capability, with the expectation that the technology in itself will also bene t from increased awareness of neural computational paradigms. This thesis focuses upon developing a methodology to study the design of neural computing systems, with an emphasis on studying systems suitable for biomedical experiments. The methodology allows for the design to be optimized according to the application. For example, di erent case studies highlight how to reduce energy consumption, reduce silicon area, or to increase network throughput. High performance processing cores are presented for both Hodgkin-Huxley and Izhikevich neurons incorporating novel design features. Further, a complete energy/area model for a neural-network-on-chip is derived, which is used in two exemplar case-studies: a cortical neural circuit to benchmark typical system performance, illustrating how a 65,000 neuron network could be processed in real-time within a 100mW power budget; and a scalable highperformance processing platform for a cerebellar neural prosthesis. From these case-studies, the contribution of network granularity towards optimal neural-network-on-chip performance is explored

Fault and Defect Tolerant Computer Architectures: Reliable Computing With Unreliable Devices

This research addresses design of a reliable computer from unreliable device technologies. A system architecture is developed for a fault and defect tolerant (FDT) computer. Trade-offs between different techniques are studied and yield and hardware cost models are developed. Fault and defect tolerant designs are created for the processor and the cache memory. Simulation results for the content-addressable memory (CAM)-based cache show 90% yield with device failure probabilities of 3 x 10(-6), three orders of magnitude better than non fault tolerant caches of the same size. The entire processor achieves 70% yield with device failure probabilities exceeding 10(-6). The required hardware redundancy is approximately 15 times that of a non-fault tolerant design. While larger than current FT designs, this architecture allows the use of devices much more likely to fail than silicon CMOS. As part of model development, an improved model is derived for NAND Multiplexing. The model is the first accurate model for small and medium amounts of redundancy. Previous models are extended to account for dependence between the inputs and produce more accurate results

Analysis and Optimization for Pipelined Asynchronous Systems

Most microelectronic chips used today--in systems ranging from cell phones to desktop computers to supercomputers--operate in basically the same way: they synchronize the operation of their millions of internal components using a clock that is distributed globally. This global clocking is becoming a critical design challenge in the quest for building chips that offer increasingly greater functionality, higher speed, and better energy efficiency. As an alternative, asynchronous or clockless design obviates the need for global synchronization; instead, components operate concurrently and synchronize locally only when necessary. This dissertation focuses on one class of asynchronous circuits: application specific stream processing systems (i.e. those that take in a stream of data items and produce a stream of processed results.) High-speed stream processors are a natural match for many high-end applications, including 3D graphics rendering, image and video processing, digital filters and DSPs, cryptography, and networking processors. This dissertation aims to make the design, analysis, optimization, and testing of circuits in the chosen domain both fast and efficient. Although much of the groundwork has already been laid by years of past work, my work identifies and addresses four critical missing pieces: i) fast performance analysis for estimating the throughput of a fine-grained pipelined system; ii) automated and versatile design space exploration; iii) a full suite of circuit level modules that connect together to implement a wide variety of system behaviors; and iv) testing and design for testability techniques that identify and target the types of errors found only in high-speed pipelined asynchronous systems. I demonstrate these techniques on a number of examples, ranging from simple applications that allow for easy comparison to hand-designed alternatives to more complex systems, such as a JPEG encoder. I also demonstrate these techniques through the design and test of a fully asynchronous GCD demonstration chip