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Applicability of neuromorphic hardware in disease spread simulations: A comparison of a SpiNNaker board and a GPU
KTH, School of Electrical Engineering and Computer Science (EECS).
KTH, School of Electrical Engineering and Computer Science (EECS).
2023 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Neuromorfisk hårdvaras tillämplighet inom smittspridningssimuleringar (Swedish)
Abstract [en]

This research paper investigates whether neuromorphic hardware can outperform the traditional GPU in simulating disease spread. As the era of Moore’s Law draws to a close, researchers are seeking alternative solutions to enhance computational power. Larger disease spread simulations, crucial for studying and assessing preventive measures, are becoming constrained as supercomputers struggle to process them quickly enough. To meet the demand for improved computational performance in disease spreading simulations, neuromorphic hardware, designed to mimic the structure and functionality of the brain, offers an intriguing alternative to current technology. By comparing the performance of a disease spread simulation implemented on a NVIDIA GPU and a SpiNNaker machine, this study demonstrates that the GPU outperforms the tested neuromorphic hardware. However, when comparing the runtime phase during which the simulation is executed, the SpiNNaker showed a constant time complexity whereas the GPU showed a linear time complexity. The study highlights the potential of neuromorphic hardware for more efficient disease spread simulations in the future, given further advancements in the technology.

Abstract [sv]

Detta forskningsarbete undersöker om neuromorfisk hårdvara kan överträffa den konventionella GPU:n vid simulering av smittspridning. I takt med att Moores lag närmar sig sitt slut, söker forskare alternativa lösningar för att förbättra beräkningskraften. Större smittspridningssimuleringar, som är avgörande för att studera och utvärdera förebyggande åtgärder, begränsas då superdatorer har svårt att bearbeta dem tillräckligt snabbt. För att möta behovet av förbättrad beräkningsprestanda vid smittspridningssimuleringar erbjuder neuromorfisk hårdvara, som är utformad för att efterlikna hjärnans struktur och funktionalitet, ett intressant alternativ till dagens teknologi. Genom att jämföra prestandan hos en smittspridningssimulering som implementerats på en NVIDIA GPU och ett Spinnakerkort visar denna studie att GPU:n presterar bättre än den testade neuromorfiska hårdvaran. Däremot visade SpiNNakerkortet en konstant tidskomplexitet jämfört med GPU:ns linjära tidskomplexitet under simuleringens exekveringsfas. Studien belyser potentialen hos neuromorfisk hårdvara för effektivare smittspridningssimuleringar i framtiden, givet vissa förbättringar inom teknologin.

Place, publisher, year, edition, pages
2023. , p. 24
Series
TRITA-EECS-EX ; 2023:306
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-330854OAI: oai:DiVA.org:kth-330854DiVA, id: diva2:1779213
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Examiners
Available from: 2023-08-01 Created: 2023-07-03 Last updated: 2023-08-01Bibliographically approved

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CiteExportLink to record
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