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Howdy All,
I just stumbled upon this research paper titled "A Diagonal State Space Model on Loihi 2 for Efficient Streaming Sequence Processing." It is currently under double-blind review for the International Conference on Learning Representations (ICLR) 2025, which will take place from 24–28 April, 2025.
As the title suggests, the paper focuses on Intel's Loihi 2. While it doesn’t mention BrainChip’s Akida technology, it is still highly relevant to us, as Loihi 2 and Akida are frequently compared as cutting-edge neuromorphic computing platforms.
The study highlights Loihi 2’s exceptional efficiency in online token-by-token inference, stating that it "consumes approximately 1000x less energy with a 75x lower latency and a 75x higher throughput compared to the recurrent implementation of n-S4D on the Jetson GPU."
It also states "our results provide the first benchmarks of an SSM on a neuromorphic hardware platform versus an edge GPU, comparing both the recurrent and convolution modes and revealing the differences in energy, latency, throughput, and task accuracy. To the best of our knowledge, this is the most holistic picture to date of the merits of neuromorphic hardware for SSM efficiency."
The authors emphasize the broader impact of their findings, stating:
"Our work and potential optimizations and extensions can be applied and tested in real-world streaming use cases, such as keyword-spotting, audio denoising, vision for drone control, autonomous driving, and other latency- or energy-constrained domains."
While I’m not as technically proficient as many in this forum, it stands to reason that if Loihi 2 demonstrates extreme efficiency in online token-by-token inference, the same should apply to BrainChip’s Akida neural processor.
If so, this would be yet another strong validation that Akida is ideally suited for applications requiring low latency and ultra-low power consumption such as robotics, autonomous vehicles, and speech enhancement.
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Extract 2
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You had me at the words stumbled upon. I started singing and movin to the song stumblin in. Anyway its one of those nights where I clearly need to sleep now lol Happy weekend fellow brners. I know I've nothing to contribute tonight apart from some sing song in my mind, but well done to all of youHowdy All,
I just stumbled upon this research paper titled "A Diagonal State Space Model on Loihi 2 for Efficient Streaming Sequence Processing." It is currently under double-blind review for the International Conference on Learning Representations (ICLR) 2025, which will take place from 24–28 April, 2025.
As the title suggests, the paper focuses on Intel's Loihi 2. While it doesn’t mention BrainChip’s Akida technology, it is still highly relevant to us, as Loihi 2 and Akida are frequently compared as cutting-edge neuromorphic computing platforms.
The study highlights Loihi 2’s exceptional efficiency in online token-by-token inference, stating that it "consumes approximately 1000x less energy with a 75x lower latency and a 75x higher throughput compared to the recurrent implementation of n-S4D on the Jetson GPU."
It also states "our results provide the first benchmarks of an SSM on a neuromorphic hardware platform versus an edge GPU, comparing both the recurrent and convolution modes and revealing the differences in energy, latency, throughput, and task accuracy. To the best of our knowledge, this is the most holistic picture to date of the merits of neuromorphic hardware for SSM efficiency."
The authors emphasize the broader impact of their findings, stating:
"Our work and potential optimizations and extensions can be applied and tested in real-world streaming use cases, such as keyword-spotting, audio denoising, vision for drone control, autonomous driving, and other latency- or energy-constrained domains."
While I’m not as technically proficient as many in this forum, it stands to reason that if Loihi 2 demonstrates extreme efficiency in online token-by-token inference, the same should apply to BrainChip’s Akida neural processor.
If so, this would be yet another strong validation that Akida is ideally suited for applications requiring low latency and ultra-low power consumption such as robotics, autonomous vehicles, and speech enhancement.
Extract 1
View attachment 77607
Extract 2
View attachment 77608
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