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DeepSeek R1's breakthrough results are a boon for BrainChip and the extreme-edge AI industry. Here's how it will impact inference at the extreme edge:
DeepSeek has shattered the "No one ever got fired for going with IBM" mentality, liberating customers from the behemoths' models. This paves the way for a Cambrian explosion of specialized LLMs delivered by smaller companies. It's akin to breaking the 4-minute mile – now that it's been done, we're poised to witness an explosion of innovation. This is excellent news for smaller companies like BrainChip.
Now imagine AI models running on whispers of energy, delivering powerful performance without massive data centers or cloud connectivity. This is the promise of extreme-edge AI, which is BrainChip's sweet spot.
While DeepSeek is an impressive transformer based model, TENNs technology, based on State Space Models (SSMs), currently offers a superior solution for extreme-edge applications:
1. No explosive KV cache: Unlike traditional transformer models that rely on rapidly expanding key-value (KV) caches, TENNs sidesteps this issue with fixed memory requirements, regardless of input length. This fundamental property enables LLMs at the extreme edge.
2. Competitive Performance
reliminary experiments pitting the DeepSeek 1.5B model against TENNs 1.2B model in various tasks, such as trip planning and simple programming, showed comparable or slightly better results for TENNs.
3. Extremely Low Training Costs: BrainChip's focus on small specialized models means training costs are less than a premium economy flight from Los Angeles to Sydney.
DeepSeek's success highlights areas where TENNs can be further enhanced. We can leverage many of the tricks learned from DeepSeek to improve their TENNs LLM even more.
The future of extreme-edge AI is bright, with DeepSeek demonstrating that small companies can compete effectively in this space.