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

2.5D Chiplet Architecture for Embedded Processing of High Velocity Streaming Data

This dissertation presents an energy efficient 2.5D chiplet-based architecture for real-time probabilistic processing of high-velocity sensor data, from an autonomous real-time ubiquitous surveillance imaging system. This work addresses problems at all levels of description. At the lowest physical level, new standard cell libraries have been developed for ultra-low voltage CMOS synthesis, as well as custom SRAM memory blocks, and mixed-signal physical true random number generators based on the perturbation of Sigma-Delta structures using random telegraph noise (RTN) in single transistor devices. At the chip level architecture, an innovative compact buffer-less switched circuit mesh network on chip (NoC) capable of reaching very high throughput (1.6Tbps), finite packet delay delivery, free from packet dropping, and free from dead-locks and live-locks, was designed for this chiplet-based solution. Additionally, a second NoC connecting processors in the network, was implemented based on token-rings, allowing access to external DDR memory. Furthermore, a new clock tree distribution network, and a wide bandwidth DRAM physical interface have been designed to address the data flow requirements within and across chiplets. At the algorithm and representation levels, the Online Change Point Detection (CPD) algorithm has been implemented for on-line learning of background-foreground segmentation. Instead of using traditional binary representation of numbers, this architecture relies on unconventional processing of signals using a bio-inspired (spike-based) unary representation of numbers, where these numbers are represented in a stochastic stream of Bernoulli random variables. By using this representation, probabilistic algorithms can be executed in a native architecture with precision on demand, where if more accuracy is required, more computational time and power can be allocated. The SoC chiplet architecture has been extensively simulated and validated using state of the art CAD methodology, and has been submitted to fabrication in a dedicated 55nm GF CMOS technology wafer run. Experimental results from fabricated test chips in the same technology are also presented

Development and testing of IEC 61850 network interference equipment - a case study

As the number of intelligent electronic devices (IEDs) is increasing in substation automation systems (SAS), the IEDs have often been extended with network communication conforming with the IEC 61850 standard, where the Generic Object-Oriented Substation Events (GOOSE) communication protocol is mostly used. This protocol sets certain demands on the IEDs and the network architecture, which must be strictly followed to ensure a safe and functional SAS. The aim of this study is to develop equipment for testing IEDs communicating with the GOOSE protocol in the worst conditions possible. The testing equipment is able to transmit Ethernet packets at the rate of one gigabit per second, in order to analyze the impact of the interference on a device under test (DUT). This testing equipment is also able search for the single most harmful packet for a DUT by using a genetic algorithm. This study shows that the developed equipment is able to find flaws in DUT's by transmitting Ethernet packets at high speeds, when setting the destination address of the interfering packets to any other address than the physical address of the DUT. However, this only works for a specific DUT, and not in every case. This particular case managed to disable the DUT's functionality completely. This study also shows that the genetic algorithms did not manage to find any specific harmful packet. This shows that the packet structure does not seem to play any role in disabling the functionality of the DUT.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Modeling of a hardware VLSI placement system: Accelerating the Simulated Annealing algorithm

An essential step in the automation of electronic design is the placement of the physical components on the target semiconductor die. The placement step presents the opportunity to reduce costs in terms of wire length and performance degradation; however it is compute intensive and is NP-complete in terms of obtaining an optimal solution. As designs have grown in complexity and gate count, obtaining an optimal solution is not feasible due to time to market constraints or sheer compute effort required. Heuristic algorithms allow for efficient but sub-optimal designs to be produced with a reduction in processing time. A widely used algorithm is Simulated Annealing (SA). The goal of this work was to develop a model that would enable an analysis into the feasibility of developing a hardware accelerated placement system which uses SA at its core. The SA heuristic was analyzed for possible improvements in efficiency with focus given to targeting the system for hardware. A solution implementing parallel computing with specialized hardware configurations inside a field programmable gate array (FPGA) was investigated as having the possibility to improve the efficiency of the SA-based algorithm. All supporting subsystems were also described for a hardware accelerated model. A large speedup was analytically shown from both accelerating the critical path of the SA algorithm as well as novel methods of improving SA\u27s efficiency. As data throughput requirements were not included in this work, the results presented may be optimistic for an overall system speedup. However, the results clearly show that future work is warranted in studying the concept of a hardware accelerated placement system

Stochastic-Based Computing with Emerging Spin-Based Device Technologies

In this dissertation, analog and emerging device physics is explored to provide a technology platform to design new bio-inspired system and novel architecture. With CMOS approaching the nano-scaling, their physics limits in feature size. Therefore, their physical device characteristics will pose severe challenges to constructing robust digital circuitry. Unlike transistor defects due to fabrication imperfection, quantum-related switching uncertainties will seriously increase their susceptibility to noise, thus rendering the traditional thinking and logic design techniques inadequate. Therefore, the trend of current research objectives is to create a non-Boolean high-level computational model and map it directly to the unique operational properties of new, power efficient, nanoscale devices. The focus of this research is based on two-fold: 1) Investigation of the physical hysteresis switching behaviors of domain wall device. We analyze phenomenon of domain wall device and identify hysteresis behavior with current range. We proposed the Domain-Wall-Motion-based (DWM) NCL circuit that achieves approximately 30x and 8x improvements in energy efficiency and chip layout area, respectively, over its equivalent CMOS design, while maintaining similar delay performance for a one bit full adder. 2) Investigation of the physical stochastic switching behaviors of Mag- netic Tunnel Junction (MTJ) device. With analyzing of stochastic switching behaviors of MTJ, we proposed an innovative stochastic-based architecture for implementing artificial neural network (S-ANN) with both magnetic tunneling junction (MTJ) and domain wall motion (DWM) devices, which enables efficient computing at an ultra-low voltage. For a well-known pattern recognition task, our mixed-model HSPICE simulation results have shown that a 34-neuron S-ANN implementation, when compared with its deterministic-based ANN counterparts implemented with digital and analog CMOS circuits, achieves more than 1.5 ~ 2 orders of magnitude lower energy consumption and 2 ~ 2.5 orders of magnitude less hidden layer chip area

Advanced analytics through FPGA based query processing and deep reinforcement learning

Today, vast streams of structured and unstructured data have been incorporated in databases, and analytical processes are applied to discover patterns, correlations, trends and other useful relationships that help to take part in a broad range of decision-making processes. The amount of generated data has grown very large over the years, and conventional database processing methods from previous generations have not been sufficient to provide satisfactory results regarding analytics performance and prediction accuracy metrics. Thus, new methods are needed in a wide array of fields from computer architectures, storage systems, network design to statistics and physics. This thesis proposes two methods to address the current challenges and meet the future demands of advanced analytics. First, we present AxleDB, a Field Programmable Gate Array based query processing system which constitutes the frontend of an advanced analytics system. AxleDB melds highly-efficient accelerators with memory, storage and provides a unified programmable environment. AxleDB is capable of offloading complex Structured Query Language queries from host CPU. The experiments have shown that running a set of TPC-H queries, AxleDB can perform full queries between 1.8x and 34.2x faster and 2.8x to 62.1x more energy efficient compared to MonetDB, and PostgreSQL on a single workstation node. Second, we introduce TauRieL, a novel deep reinforcement learning (DRL) based method for combinatorial problems. The design idea behind combining DRL and combinatorial problems is to apply the prediction capabilities of deep reinforcement learning and to use the universality of combinatorial optimization problems to explore general purpose predictive methods. TauRieL utilizes an actor-critic inspired DRL architecture that adopts ordinary feedforward nets. Furthermore, TauRieL performs online training which unifies training and state space exploration. The experiments show that TauRieL can generate solutions two orders of magnitude faster and performs within 3% of accuracy compared to the state-of-the-art DRL on the Traveling Salesman Problem while searching for the shortest tour. Also, we present that TauRieL can be adapted to the Knapsack combinatorial problem. With a very minimal problem specific modification, TauRieL can outperform a Knapsack specific greedy heuristics.Hoy en día, se han incorporado grandes cantidades de datos estructurados y no estructurados en las bases de datos, y se les aplican procesos analíticos para descubrir patrones, correlaciones, tendencias y otras relaciones útiles que se utilizan mayormente para la toma de decisiones. La cantidad de datos generados ha crecido enormemente a lo largo de los años, y los métodos de procesamiento de bases de datos convencionales utilizados en las generaciones anteriores no son suficientes para proporcionar resultados satisfactorios respecto al rendimiento del análisis y respecto de la precisión de las predicciones. Por lo tanto, se necesitan nuevos métodos en una amplia gama de campos, desde arquitecturas de computadoras, sistemas de almacenamiento, diseño de redes hasta estadísticas y física. Esta tesis propone dos métodos para abordar los desafíos actuales y satisfacer las demandas futuras de análisis avanzado. Primero, presentamos AxleDB, un sistema de procesamiento de consultas basado en FPGAs (Field Programmable Gate Array) que constituye la interfaz de un sistema de análisis avanzado. AxleDB combina aceleradores altamente eficientes con memoria, almacenamiento y proporciona un entorno programable unificado. AxleDB es capaz de descargar consultas complejas de lenguaje de consulta estructurado desde la CPU del host. Los experimentos han demostrado que al ejecutar un conjunto de consultas TPC-H, AxleDB puede realizar consultas completas entre 1.8x y 34.2x más rápido y 2.8x a 62.1x más eficiente energéticamente que MonetDB, y PostgreSQL en un solo nodo de una estación de trabajo. En segundo lugar, presentamos TauRieL, un nuevo método basado en Deep Reinforcement Learning (DRL) para problemas combinatorios. La idea central que está detrás de la combinación de DRL y problemas combinatorios, es aplicar las capacidades de predicción del aprendizaje de refuerzo profundo y el uso de la universalidad de los problemas de optimización combinatoria para explorar métodos predictivos de propósito general. TauRieL utiliza una arquitectura DRL inspirada en el actor-crítico que se adapta a redes feedforward. Además, TauRieL realiza el entrenamieton en línea que unifica el entrenamiento y la exploración espacial de los estados. Los experimentos muestran que TauRieL puede generar soluciones dos órdenes de magnitud más rápido y funciona con un 3% de precisión en comparación con el estado del arte en DRL aplicado al problema del viajante mientras busca el recorrido más corto. Además, presentamos que TauRieL puede adaptarse al problema de la Mochila. Con una modificación específica muy mínima del problema, TauRieL puede superar a una heurística codiciosa de Knapsack Problem.Postprint (published version

Automatic generation of high-throughput systolic tree-based solvers for modern FPGAs

Tree-based models are a class of numerical methods widely used in financial option pricing, which have a computational complexity that is quadratic with respect to the solution accuracy. Previous research has employed reconfigurable computing with small degrees of parallelism to provide faster hardware solutions compared with general-purpose processing software designs. However, due to the nature of their vector hardware architectures, they cannot scale their compute resources efficiently, leaving them with pricing latency figures which are quadratic with respect to the problem size, and hence to the solution accuracy. Also, their solutions are not productive as they require hardware engineering effort, and can only solve one type of tree problems, known as the standard American option. This thesis presents a novel methodology in the form of a high-level design framework which can capture any common tree-based problem, and automatically generates high-throughput field-programmable gate array (FPGA) solvers based on proposed scalable hardware architectures. The thesis has made three main contributions. First, systolic architectures were proposed for solving binomial and trinomial trees, which due to their custom systolic data-movement mechanisms, can scale their compute resources efficiently to provide linear latency scaling for medium-size trees and improved quadratic latency scaling for large trees. Using the proposed systolic architectures, throughput speed-ups of up to 5.6X and 12X were achieved for modern FPGAs, compared to previous vector designs, for medium and large trees, respectively. Second, a productive high-level design framework was proposed, that can capture any common binomial and trinomial tree problem, and a methodology was suggested to generate high-throughput systolic solvers with custom data precision, where the methodology requires no hardware design effort from the end user. Third, a fully-automated tool-chain methodology was proposed that, compared to previous tree-based solvers, improves user productivity by removing the manual engineering effort of applying the design framework to option pricing problems. Using the productive design framework, high-throughput systolic FPGA solvers have been automatically generated from simple end-user C descriptions for several tree problems, such as American, Bermudan, and barrier options.Open Acces

Using commercial FPGAs as external accelerators for artificial neural networks in embedded applications

Capstone Project submitted to the Department of Engineering, Ashesi University in partial fulfillment of the requirements for the award of Bachelor of Science degree in Computer Engineering, May 2020Artificial Neural Networks (ANNs) is a branch of Machine Learning that has seen recent widespread adoption for solving computational problems that seem impractical to solve with traditional algorithmic approaches. ANNs have achieved high accuracy on tasks such as facial recognition, object detection and speech recognition. And recently, ANNs have also seen applications in embedded systems, where it has been used to train robots to learn from their environment and cameras to detect faces in a crowd. However, achieving reasonable performance on a traditional microcontroller can be difficult since ANNs are computationally expensive. This paper investigates the possibility of using a Field Programmable Gate Array (FPGA) as an external accelerator for a microcontroller unit. The aim is for the combined performance of the FPGA and the microcontroller, for running the ANN, to be better than just the microcontroller. For the tested neural network, the results show that the combined system with the FPGA and microcontroller runs at more than twice the speed of a system with just a microcontroller.Ashesi Universit

Interconnect technologies for very large spiking neural networks

In the scope of this thesis, a neural event communication architecture has been developed for use in an accelerated neuromorphic computing system and with a packet-based high performance interconnection network. Existing neuromorphic computing systems mostly use highly customised interconnection networks, directly routing single spike events to their destination. In contrast, the approach of this thesis uses a general purpose packet-based interconnection network and accumulates multiple spike events at the source node into larger network packets destined to common destinations. This is required to optimise the payload efficiency, given relatively large packet headers as compared to the size of neural spike events. Theoretical considerations are made about the efficiency of different event aggregation strategies. Thereby, important factors are the number of occurring event network-destinations and their relative frequency, as well as the number of available accumulation buffers. Based on the concept of Markov Chains, an analytical method is developed and used to evaluate these aggregation strategies. Additionally, some of these strategies are stochastically simulated in order to verify the analytical method and evaluate them beyond its applicability. Based on the results of this analysis, an optimisation strategy is proposed for the mapping of neural populations onto interconnected neuromorphic chips, as well as the joint assignment of event network-destinations to a set of accumulation buffers. During this thesis, such an event communication architecture has been implemented on the communication FPGAs in the BrainScaleS-2 accelerated neuromorphic computing system. Thereby, its usability can be scaled beyond single chip setups. For this, the EXTOLL network technology is used to transport and route the aggregated neural event packets with high bandwidth and low latency. At the FPGA, a network bandwidth of up to 12 Gbit/s is usable at a maximum payload efficiency of 94 %. The latency has been measured in the scope of this thesis to a range between 1.6 μs and 2.3 μs across the network between two neuron circuits on separate chips. This latency is thereby mostly dominated by the path from the neuromorphic chip across the communication FPGA into the network and back on the receiving side. As the EXTOLL network hardware itself is clocked at a much higher frequency than the FPGAs, the latency is expected to scale in the order of only approximately 75 ns for each additional hop through the network. For being able to globally interpret the arrival timestamps that are transmitted with every spike event, the system time counters on the FPGAs are synchronised across the network. For this, the global interrupt mechanism implemented in the EXTOLL hardware is characterised and used within this thesis. With this, a synchronisation accuracy of ±40ns could be measured. At the end of this thesis, the successful emulation of a neural signal propagation model, distributed across two BrainScaleS-2 chips and FPGAs is demonstrated using the implemented event communication architecture and the described synchronisation mechanism