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  • content
    • blog
      • digital-neuromorphic-hardware-read-list
      • efficient-compression-event-based-data-neuromorphic-applications
      • northpole-ibm-neuromorphic-ai-hardware
      • open-neuromorphic-evolves-charter-first-executive-committee-election
      • spiking-neural-network-framework-benchmarking
      • spiking-neurons-digital-hardware-implementation
      • truenorth-deep-dive-ibm-neuromorphic-chip-design
    • contributors

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content/blog/digital-neuromorphic-hardware-read-list/index.md

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image: banner.png
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tags: ["research", "hardware", "digital", "neuromorphic", "snn", "AI"]
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show_author_bios: true
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---
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Here's a list of articles and theses related to digital hardware designs for neuomorphic applications. I plan to update it regularly. To be redirected directly to the sources, click on the titles!
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## Credits
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The cover image is taken from [Charlotte Frenkel](https://ch.frenkel.github.io)'s thesis.
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## Authors
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- [Fabrizio Ottati](https://fabrizio-ottati.dev) is a Ph.D. student in the HLS Laboratory of the Department of Electronics and Communications, Politecnico di Torino. His main interests are event-based cameras, digital hardware design and neuromorphic computing. He is one of the maintainers of two open source projects in the field of neuromorphic computing, [Tonic](/neuromorphic-computing/software/data-tools/tonic/) and [Expelliarmus](/neuromorphic-computing/software/data-tools/expelliarmus/), and one of the founders of [Open Neuromorphic](https://open-neuromorphic.org).

content/blog/efficient-compression-event-based-data-neuromorphic-applications/index.md

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- Alexandre Marcireau
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image: file_read_benchmark.png
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tags: ["file encoding", "events", "event camera", "compression"]
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show_author_bios: true
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---
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## Datasets grow larger in size
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## Summary
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When training spiking neural networks on event-based data, we want to be able to feed new data to the network as fast as possible. But given the high data rate of an event camera, the amount of data quickly becomes an issue itself, especially for more complex tasks. So we want to choose a good trade-off between a dataset size that's manageable and reading speed. We hope that this article will help future groups that record large-scale datasets to pick a good encoding format.
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## Authors
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* [Gregor Lenz](https://lenzgregor.com) is a research engineer at SynSense, where he works on machine learning pipelines that can train and deploy robust models on neuromorphic hardware. He holds a PhD in neuromorphic engineering from Sorbonne University in Paris, France.
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* [Fabrizio Ottati](https://fabrizio-ottati.dev) is a Ph.D. student in the HLS Laboratory of the Department of Electronics and Communications, Politecnico di Torino. His main interests are event-based cameras, digital hardware design and neuromorphic computing. He is one of the maintainers of two open source projects in the field of neuromorphic computing, [Tonic](/neuromorphic-computing/software/data-tools/tonic/) and [Expelliarmus](/neuromorphic-computing/software/data-tools/expelliarmus/), and one of the founders of [Open Neuromorphic](https://open-neuromorphic.org).
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* [Alexandre Marcireau](https://github.com/aMarcireau).
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## Comments
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The aedat4 file contains IMU events as well as change detection events, which increases the file size artificially in contrast to the other benchmarked formats.

content/blog/northpole-ibm-neuromorphic-ai-hardware/index.md

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author:
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- Fabrizio Ottati
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---
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[NorthPole](https://research.ibm.com/blog/northpole-ibm-ai-chip) is the new

content/blog/open-neuromorphic-evolves-charter-first-executive-committee-election/index.md

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- Gregor Lenz
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tags: ["community", "governance", "election", "charter", "AGM", "open source"]
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Exciting news from the Open Neuromorphic (ONM) community! As our community continues to grow and thrive, we're taking an important step towards a more structured and sustainable future. Today, we're announcing the introduction of an **ONM Organisational Charter** and the upcoming election of our very first **Executive Committee**.

content/blog/spiking-neural-network-framework-benchmarking/index.md

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- "Jens E. Pedersen"
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image: neurons-race.png
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tags: ["snn", "framework", "library", "pytorch", "JAX"]
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## Introduction
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* given equivalent forward dynamics, check gradient correlations and magnitudes
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* ✔ memory consumption of different libraries
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* ✔ add JAX framework
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## Authors
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* [Gregor Lenz](https://lenzgregor.com) holds a PhD in neuromorphic engineering from Sorbonne University and has been training SNNs for a little while now!
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* [Kade Heckel](https://github.com/kmheckel/spyx) is a Marshall Scholar and a current MPhil student at the Univeristy of Cambridge. He authored the library Spyx as part of the dissertation for an MSc at the University of Sussex under the supervision of Thomas Nowotny.
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* [Sumit Bam Shrestha](https://www.intel.com/content/www/us/en/research/featured-researchers/sumit-bam-shrestha.html) is a research scientist in neuromorphic computing at Intel Labs, where he focuses on deep spiking neural networks.
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* [Cameron Barker](https://github.com/cameron-git) is a Neuromorphic Engineer at Ripga ltd.
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* [Jens Egholm Pedersen](https://jepedersen.dk) is a PhD student at KTH Royal Institute of Technology

content/blog/spiking-neurons-digital-hardware-implementation/index.md

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tags: ["hardware", "digital", "spiking", "snn", "rtl", "verilog", "AI", "machine learning"]
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## Introduction
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The cover image is the Loihi die, taken from [WikiChip](https://en.wikichip.org/wiki/intel/loihi).
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## Authors
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- [Fabrizio Ottati](https://fabrizio-ottati.dev) is a Ph.D. student in the HLS Laboratory of the Department of Electronics and Communications, Politecnico di Torino. His main interests are event-based cameras, digital hardware design and neuromorphic computing. He is one of the maintainers of two open source projects in the field of neuromorphic computing, [Tonic](https://tonic.readthedocs.io) and [Expelliarmus](https://expelliarmus.readthedocs.io), and one of the founders of [Open Neuromorphic](https://open-neuromorphic.org).
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## Bibliography
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- [*Loihi: A Neuromorphic Manycore Processor with On-Chip Learning*](https://redwood.berkeley.edu/wp-content/uploads/2021/08/Davies2018.pdf), Mike Davies et al., 2018.

content/blog/truenorth-deep-dive-ibm-neuromorphic-chip-design/index.md

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## Why do we want to emulate the brain?
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*Stay tuned for episode II, in which we will dive in the theory behind the asynchronous design of TrueNorth :)*
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## Authors
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- [Fabrizio Ottati](https://fabrizio-ottati.dev) is a Ph.D. student in the HLS Laboratory of the Department of Electronics and Communications, Politecnico di Torino. His main interests are event-based cameras, digital hardware design and neuromorphic computing. He is one of the maintainers of two open source projects in the field of neuromorphic computing, [Tonic](https://tonic.readthedocs.io) and [Expelliarmus](https://expelliarmus.readthedocs.io), and one of the founders of [Open Neuromorphic](https://open-neuromorphic.org).
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## Bibliography
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* [*TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip*](https://redwood.berkeley.edu/wp-content/uploads/2021/08/Akopyan2015.pdf), Filipp Akopyan et al.
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* [*Foundations of neuroscience - The neuron*](https://openbooks.lib.msu.edu/neuroscience/chapter/the-neuron/), Casey Henley, Michigan State University.
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title: "Alexandre Marcireau"
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description: "Software developer in neuromorphic computing, recognized for his contributions to the Faery event processing library for event-based camera data."
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social:
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link: "https://github.com/aMarcireau"
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title: "github"
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Alexandre Marcireau is a software developer and contributor in the neuromorphic computing space, known for his work on the Faery event processing library.

content/contributors/cameron-barker/index.md

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description: "Neuromorphic Engineer at Ripga ltd., contributing his expertise to the development of neuromorphic technologies and applications."
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social:
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link: "https://github.com/cameron-git"
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title: "linkedin"
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[Cameron Barker](https://github.com/cameron-git) is a Neuromorphic Engineer at Ripga ltd.
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Cameron Barker is a Neuromorphic Engineer at Ripga ltd.
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content/contributors/sumit-bam-shrestha/index.md

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title: "Sumit Bam Shrestha"
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description: "Research scientist at Intel Labs, focusing on deep spiking neural networks. Contributor to the Lava software framework and Loihi hardware applications."
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image: "" # Photo needed: sumit-bam-shrestha.jpg or similar
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social:
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- icon: "fa fa-house"
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link: "https://www.intel.com/content/www/us/en/research/featured-researchers/sumit-bam-shrestha.html"
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title: "website"
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Sumit Bam Shrestha is a research scientist in neuromorphic computing at Intel Labs, where he focuses on deep spiking neural networks. He is a contributor to the Lava software framework and has worked on applying neuromorphic hardware like Loihi to various tasks.
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Sumit Bam Shrestha is a research scientist in neuromorphic computing at Intel Labs, where he focuses on deep spiking neural networks. He is a contributor to the Lava software framework and has worked on applying neuromorphic hardware like Loihi to various tasks.

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