Tops Best Peak Performance

Based on the AI-embedded Neural Network Processor (NPU), the NCC S1 possesses 28,000 parallel neural computing coresand supports on-chip parallel and in-situ calculations. Its peak up to 5.6Tops, dozens of times higher than othersolutions on the market. It can afford complex high-density calculations for high-performance edge computing field.

AI Processing Framework APiM

Based on AI-specific MPE matrix engine and APiM (AI processing in Memory) framework, it deals with AI in arevolutionary way. Without any instructions, bus and external DDR cache, plenty of data can be directly input or outputto the silicon chip by upgrading the network preloading once, which greatly lifts the processing speed of AI andreduces the processing energy consumption.

Tops/W High Energy Efficiency

The NPU of NCC S1 neural network computing card uses the 28nm process technology. The power is only 300mW whenthroughput is 2.8 Tops, while the energy efficiency is up to 9.3 Tops/W. It maintains strong computing ability whileowning extremely low energy consumption, endowed with great advantages in the edge computing field of terminalequipment.

High-performance Hardware Platform

NCC S1 neural network computing card can be equipped with ROC-RK3399-PC open source main board. On condition that it isstocked with high-performance RK3399 six-core processor and abundant hardware interface, it can rapidly integratehardware platform for edge computing, set up product prototype, and thus accelerate AI product project process.

Supporting Model Training Tools

Provide the complete and easy-to-use model training tool PLAI (People Learn AI) which based on PyTorch, it can bedeveloped on Windows 10 and Ubuntu 16.04 systems to add custom network models more easily and quickly, which greatlyreduces the technical difficulties to applying AI and makes AI technology accessible to more people.

Provide Network Training Model

Support the following three network training model examples such as GNet1, GNet18 and GNetfc with more networkinstances continuing to emerge subsequently, making it possible to easily test a large number of deep learningapplications on the device.