NeuronFabric: A Software Reference Architecture for On-Chip Transformer Training with Local Adam
arXiv:2606.16440v1 Announce Type: cross Abstract: Publicly documented accelerator architectures generally separate training computation from optimizer-state updates or rely on external memory and host orchestration. This paper presents NeuronFabric, a software reference architecture intended for future FPGA and ASIC implementations of transformer training with local Adam updates. A complete C# prototype implements forward pass, backpropagation, and Adam optimization without external machine-learning frameworks. The goal is to validate numerical correctness and memory requirements before hardwa
The increasing computational demands of transformer models and the limitations of current accelerator architectures are driving innovation in on-chip training methods.
This development could significantly reduce the cost and energy consumption of advanced AI training, accelerating model development and deployment, particularly for specialized applications.
Current reliance on external memory and host orchestration for AI training could diminish as more integrated on-chip solutions become viable, enhancing efficiency and decentralization opportunities.
- · FPGA/ASIC manufacturers
- · AI hardware developers
- · Cloud providers with specialized AI offerings
- · AI model developers
- · Manufacturers of generic high-bandwidth memory dependent on host orchestration
- · Traditional CPU-centric AI training solutions
On-chip transformer training becomes more efficient and widespread, reducing the computational budget required for state-of-the-art AI.
Decentralized and edge-based AI training capabilities could expand, enabling more specialized and private model development.
New AI applications become feasible due to lower power and cost footprints, potentially democratizing access to powerful AI training infrastructure.
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Read at arXiv cs.AI