The field of neuromorphic computing concerns simulating the information processing of a brain in software or hardware on a computing platform. One neuromorphic platform that uses specialized hardware is SpiNNaker. It is an integrated circuit consisting of multiple general purpose processing cores that can run simulations of neurons. A custom on-chip network mimics the high level of neuron interconnectedness in a brain. The second generation of this chip is currently in development and a prototype, JiB 2, is used in this thesis. This chip has a Ball Grid Array (BGA) footprint and requires several supply voltage levels to operate making implementation more complex. To use such a chip in an autonomous robot, the hardware needs to be in a small form factor. It is beneficial to use an intermediary platform with support for many actuators and sensors to avoid having to develop new drivers (and because the processing power of individual blocks in JiB 2 is not well suited to these tasks). This thesis shows how a platform for autonomous use in robots can be designed with the current prototype chip. It details the design decisions made for the power supply and using the footprint. The existing software is explained and modifications made are shown. Some performance metrics (memory requirements, power and cost) are characterized. A simple program running on the prototype chip with input and output from a microcontroller development board using STM32 is demonstrated. This project suggests a path to deploy software on the JiB 2 and let it interact with the physical world. / Att i en dator eller speciell hårdvara simulera hur neuroner i en hjärna interagerar i sitt informationsutbyte studeras inom fältet neouromorfisk databehandling. Eftersom utbytet sker med snabba länkar mellan många oberoende enheter är traditionell datorhårdvara inte lämpad att implementera sådana skeenden. Därför finns specialhårdvara som bättre efterlikar detta utbyte genom att, till exempel, använda många enkla processorkärnor (för att simulera neuroner) tillsammans med ett snabbt nätverk på kretsen (och mellan flera kretsar). Ett användningsområde är i större komplexa system men det finns en efterfrågan att kunna använda den även i mer begränsade kontexter. En sådan specialhårdvara är den integrerade kretsen SpiNNaker (Spiking Neural Network Architecture). En andra generationen av den kretsen är under utveckling och projektet i denna uppsats har arbetat med en begränsad prototyp kallad JiB2. Målet har varit att bygga en plattform som visar hur JiB 2 kan utnyttjas fristående i en robot. Detta kräver hårdvara som är möjlig att enkelt ladda med nya program. Den behöver klara att strömförsörja kretsen från exempelvis ett batteri. Den ska också ha möjlighet att koppla in- och utsignaler till programmet som körs i specialkretsen. Detta arbete visar att hårdvara går att tillverka i en storlek som lämpar sig för använding i robotar. Ett flöde för utveckling och drifttagning av programvara till plattformen demonstreras.

It is believed that neuromorphic hardware will accelerate neuroscience research and enable the next generation edge AI. On the other hand, brain-inspired algorithms are supposed to work efficiently on neuromorphic hardware. But both processes don't happen automatically. To efficiently bring together hardware and algorithm, optimizations are necessary based on the understanding of both sides. In this work, software frameworks and optimizations for efficient implementation of neural network-based algorithms on SpiNNaker 2 are proposed, resulting in optimized power consumption, memory footprint and computation time. In particular, first, a software framework including power management strategies is proposed to apply dynamic voltage and frequency scaling (DVFS) to the simulation of spiking neural networks, which is also the first-ever software framework running a neural network on SpiNNaker 2. The result shows the power consumption is reduced by 60.7% in the synfire chain benchmark. Second, numerical optimizations and data structure optimizations lead to an efficient implementation of reward-based synaptic sampling, which is one of the most complex plasticity algorithms ever implemented on neuromorphic hardware. The results show a reduction of computation time by a factor of 2 and energy consumption by 62%. Third, software optimizations are proposed which effectively exploit the efficiency of the multiply-accumulate array and the flexibility of the ARM core, which results in, when compared with Loihi, 3 times faster inference speed and 5 times lower energy consumption in a keyword spotting benchmark, and faster inference speed and lower energy consumption for adaptive control benchmark in high dimensional cases. The results of this work demonstrate the potential of SpiNNaker 2, explore its range of applications and also provide feedback for the design of the next generation neuromorphic hardware.