Kneron
Empowering Edge AI with high-efficiency reconfigurable NPUs and decentralized AI platforms.
Revolutionizing edge intelligence through Analog Compute-in-Memory technology for extreme power efficiency.
Mythic AI represents a paradigm shift in AI inference hardware, utilizing Analog Compute-in-Memory (CiM) to overcome the traditional von Neumann bottleneck. By performing matrix multiplications directly within flash memory cells using analog signal processing, the Mythic Analog Matrix Processor (AMP) achieves up to 10x the power efficiency and throughput of traditional digital DSPs and GPUs. Their 2026 market position is solidified by the M1076 and subsequent M2000 series, which cater to high-density video analytics and complex spatial computing. The technical architecture relies on the Mythic SDK, which handles the complex translation of digital weights into analog conductance levels, providing a seamless deployment path for PyTorch and TensorFlow models. Unlike digital accelerators that require constant DRAM access, Mythic's architecture stores the entire model on-chip, drastically reducing latency and energy consumption. This makes it a critical solution for power-constrained environments such as autonomous drones, medical imaging devices, and smart industrial sensors where sub-watt performance for multi-stream AI is a requirement.
Uses flash memory cells to store weights as conductance levels, performing calculations using Ohm's and Kirchhoff's laws.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Advanced compiler techniques that exploit model sparsity to reduce analog noise and improve throughput.
The entire model is stored in non-volatile memory on-chip, removing the need for external DDR memory.
Executes inference cycles in a fixed number of clock cycles regardless of input data variance.
A proprietary compiler that optimizes neural network graphs for the physical layout of analog tiles.
Interconnect architecture allowing multiple AMPs to work in parallel on a single PCIe bus.
Quantization-aware training (QAT) tools that simulate analog variations during the fine-tuning phase.
Processing hundreds of 4K camera streams in real-time with limited power budget at the edge.
Registry Updated:2/7/2026
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