Frangipani
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Yes, that statement actually comes from a paper presented at the IEEE ISVLSI conference in July 2023. The paper is titled "Comparative Analysis of Low-Power Anomaly Detection on Neuromorphic Chips for Controller Area Network (CAN) Data", and was co-authored by Tarek Taha (University of Dayton) and Simon Khan (AFRL), among others.
In the study, they implemented an anomaly detection model on both Loihi and Akida chips, using the same CAN dataset. The results showed that Loihi consumed around 91 times less power than Akida for this specific task.
However, it's important to note that this is not a general benchmark. The test was very specific to a certain use-case in vehicular networks, and the result depends heavily on the software setup, optimization level, and even how the power consumption was meassured. Also, Loihi is more of a research platform, while Akida targets edge applications with different design priorities.
So the result is interesting, but should'nt be overgeneralized.
Hi @Baneino,
could you please share with us where you found evidence of that July 2023 IEEE ISVLSI conference paper titled “Comparative Analysis of Low-Power Anomaly Detection on Neuromorphic Chips for Controller Area Network (CAN) Data”, which you referred to in your above post?
Neither is it included in Tarek Taha’s extensive list of publications (which, while not quite up to date, should be exhaustive with respect to those published in 2023) nor could I find any trace of it when googling the paper’s title as such (except for your 28 July TSE post, that is).
The only conference paper listed on Tarek Taha’s website, which appears to be relevant to the topic, happens to be the 2024 paper “Unsupervised Anomaly Detection for Automotive CAN Bus on the Intel Loihi”:
Publications
www.taha-lab.org
This is also the only paper on this topic I was able to find under Simon Khan’s Google Scholar profile, who - according to you - is one of the other co-authors.
Said 2024 conference paper was not a comparative analysis, though, plus the authors stated in their abstract that “To the best of our knowledge, this is the first low-power, unsupervised anomaly detection system using the Loihi or any other neuromorphic processor”, which obviously wouldn’t make sense if they had already published an earlier paper comparing Loihi and Akida.
(https://ieeexplore.ieee.org/document/10651224/)
Then there is also the 6 August 2025 conference paper titled “Anomaly Detection of CAN bus messages on neuromorphic hardware” that I referred to in April, which equally appears to contradict the existence of a prior comparative paper by the same authors (cf. section underlined in red):
It appears not everyone at AFRL resp. collaborating with AFRL would agree that Akida is always the superior choice:
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Anomaly detection of CAN bus messages on neuromorphic hardware
Anomaly detection is becoming an essential component of the modern automotive industry. With the increasing prevalence of smart vehicles, the number of Electronic Control Units (ECUs) integrated within automotive systems is also growing. These ECUs communicate through the Controller Area Network...spie.org
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Tarek Taha
taha-lab.org
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Is that mysteriously elusive 2023 conference paper you claimed existed by any chance a GenAI hallucination?