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

On microelectronic self-learning cognitive chip systems

After a brief review of machine learning techniques and applications, this Ph.D. thesis examines several approaches for implementing machine learning architectures and algorithms into hardware within our laboratory. From this interdisciplinary background support, we have motivations for novel approaches that we intend to follow as an objective of innovative hardware implementations of dynamically self-reconfigurable logic for enhanced self-adaptive, self-(re)organizing and eventually self-assembling machine learning systems, while developing this new particular area of research. And after reviewing some relevant background of robotic control methods followed by most recent advanced cognitive controllers, this Ph.D. thesis suggests that amongst many well-known ways of designing operational technologies, the design methodologies of those leading-edge high-tech devices such as cognitive chips that may well lead to intelligent machines exhibiting conscious phenomena should crucially be restricted to extremely well defined constraints. Roboticists also need those as specifications to help decide upfront on otherwise infinitely free hardware/software design details. In addition and most importantly, we propose these specifications as methodological guidelines tightly related to ethics and the nowadays well-identified workings of the human body and of its psyche

An FPGA Based Hardware Accelerator for Remote Surveillance Cameras

The Blackeye II camera, produced by Kinopta, is used for remote security, conservation and traffic flow surveillance. The camera uses an image sensor to acquire photographs which undergo image processing and JPEG encoding on a microprocessor. Although the microprocessor performs other tasks, it is the processing and encoding of images that limit the frame rate of the camera to 2 frames per second (fps). Clients have requested an increase to 12.5 fps while adding more image processing to each photograph. The current microprocessor-based system is unable to achieve this. Custom digital logic systems perform well on processes that naturally form a pipeline, such as the Blackeye II image processing system. This project develops a digital logic system based on an FPGA to receive images from the image sensor, perform the required image processing operations, encode the images in JPEG format and send them on to the microprocessor. The objective is to implement a proof of concept device based upon the Blackeye II’s existing hardware and an FPGA development board. It will implement the proposed pipeline including one example of an image processing operation. A JPEG encoder is designed to process the 752 × 480 greyscale photographs from the image processor in real time. The JPEG encoder consists of four stages: discrete cosine transform (DCT), quantisation, zig-zag buffer and Huffman encoder. The DCT design is based upon the work of Woods et al. [1], which is improved on. An analysis of the relationship between precision and accuracy in the DCT and quantisation stages is used to minimise the system’s resource requirements. The JPEG encoder is successfully tested in simulation. Input and output stages are added to the design. The input stage receives data from the image sensor and removes breaks in the data stream. The output stage must concatenate the data from the JPEG encoder and transmit it to the microprocessor via the microprocessor’s ISI (image sensor interface) peripheral. An image sharpening filter is developed and inserted into the pipeline between the input and JPEG encoder. Because remote surveillance cameras are battery powered, the minimisation of power consumption is a key concern. To minimise power consumption a mechanism is introduced to track those modules in the pipeline that are in use at any time. Any not in use are paused by gating the module’s clock source. Once the system is complete and tested in simulation it is loaded into hardware. The FPGA development board is attached to the image sensor board and microprocessor board of the Blackeye II camera by a purpose-built breakout board. Plugging the microprocessor board into a PC provides a live stream of images proving the successful operation of the FPGA system. The project objectives were exceeded by increasing the frame rate of the Blackeye II to 20 fps, which will not decrease with additional image processing operations. The project was viewed as a success by Kinopta, who have committed to its further development

Dynamically reconfigurable architecture for embedded computer vision systems

The objective of this research work is to design, develop and implement a new architecture which integrates on the same chip all the processing levels of a complete Computer Vision system, so that the execution is efficient without compromising the power consumption while keeping a reduced cost. For this purpose, an analysis and classification of different mathematical operations and algorithms commonly used in Computer Vision are carried out, as well as a in-depth review of the image processing capabilities of current-generation hardware devices. This permits to determine the requirements and the key aspects for an efficient architecture. A representative set of algorithms is employed as benchmark to evaluate the proposed architecture, which is implemented on an FPGA-based system-on-chip. Finally, the prototype is compared to other related approaches in order to determine its advantages and weaknesses

Build framework and runtime abstraction for partial reconfiguration on FPGA SoCs

Growth in edge computing has increased the requirement for edge systems to process larger volumes of real-time data, such as with image processing and machine learning; which are increasingly demanding of computing resources. Offloading tasks to the cloud provides some relief but is network dependant, high latency and expensive. Alternative architectures such as GPUs provide higher performance acceleration for this type of data processing but trade processing performance for an increase in power consumption. Another option is the Field Programmable Gate Array; a flexible matrix of logic that can be configured by a designer to provide a highly optimised computation path for incoming data. There are drawbacks; the FPGA design process is complex, the domain is dissimilar to software and the tools require bespoke expertise. A designer must manage the hardware to software paradigm introduced when tightly-coupled with general purpose processor. Advanced features, such as the ability to partially reconfigure (PR) specific regions of the FPGA, further increase this complexity. This thesis presents theory and demonstration of custom frameworks and tools for increasing abstraction and simplifying control over PR applications. We present mechanisms for networked PR; a mechanism for bypassing the traditional software networking stack to trigger PR with reduced latency and increased determinism. We developed a build framework for automating the end-to-end PR design process for Linux based systems as well as an abstracted runtime for managing the resulting applications. Finally, we take expand on this work and present a high level abstraction for PR on cyber physical systems, with a demonstration using the Robot Operating System. This work is released as open source contributions, designed to enable future PR research

Intrinsically Evolvable Artificial Neural Networks

Dedicated hardware implementations of neural networks promise to provide faster, lower power operation when compared to software implementations executing on processors. Unfortunately, most custom hardware implementations do not support intrinsic training of these networks on-chip. The training is typically done using offline software simulations and the obtained network is synthesized and targeted to the hardware offline. The FPGA design presented here facilitates on-chip intrinsic training of artificial neural networks. Block-based neural networks (BbNN), the type of artificial neural networks implemented here, are grid-based networks neuron blocks. These networks are trained using genetic algorithms to simultaneously optimize the network structure and the internal synaptic parameters. The design supports online structure and parameter updates, and is an intrinsically evolvable BbNN platform supporting functional-level hardware evolution. Functional-level evolvable hardware (EHW) uses evolutionary algorithms to evolve interconnections and internal parameters of functional modules in reconfigurable computing systems such as FPGAs. Functional modules can be any hardware modules such as multipliers, adders, and trigonometric functions. In the implementation presented, the functional module is a neuron block. The designed platform is suitable for applications in dynamic environments, and can be adapted and retrained online. The online training capability has been demonstrated using a case study. A performance characterization model for RC implementations of BbNNs has also been presented

A Study of Multiprocessor Systems using the Picoblaze 8-bit Microcontroller Implemented on Field Programmable Gate Arrays

As Field Programmable Gate Arrays (FPGAs) are becoming more capable of implementing complex logic circuits, designers are increasingly choosing them over traditional microprocessor-based systems for implementing digital controllers and digital signal processing applications. Indeed, as FPGAs are being built using state-of-the-art deep submicron CMOS processes, the increased amount of logic and memory resources allows such FPGA-based implementations to compete in terms of speed, complexity, and power dissipation with most custom-built chips, but at a fraction of the development costs. The modern FPGA is now capable of implementing multiple instances of configurable processors that are completely specified by a high-level descriptor language. Such arrays of soft processor cores have opened up new design possibilities that include complex embedded systems applications that were previously implemented by custom multiprocessor chips. As the FPGA-based multiprocessor system is completely configurable by the user, it can be optimized for speed and power dissipation to fit a given application. The goal of this thesis is to investigate design methods for implementing an array of soft processor cores using the Xilinx FPGA-based 8-bit microcontroller known as PicoBlaze. While development tools exist for the larger 32-bit processor from Xilinx known as MicroBlaze, no such resources are currently available for the PicoBlaze microcontroller. PicoBlaze benefits in applications that requires only less data bits (less than 8 bits). For example, consider the gene sequencing or DNA sequencing in which the processing requires only 2 to 5 bits. In such an application, PicoBlaze can be a simple processor to produce the results. Also, the PicoBlaze unit offers a finer level of granularity and hence consumes fewer resources than the larger 32-bit MicroBlaze processor. Hence, the former will find applications in embedded systems requiring a complex design to be partitioned over several processors but where only an 8-bit datapath is required