Edge Ai Hardware Architechture

If the section on Machine Learning was the "Soul" (the logic) and Data Engineering was the "Fuel" (the data), this section on Edge AI Hardware is the "Body." To achieve true Microwatt-scale intelligence, we must move away from the energy-intensive paradigms of general-purpose CPUs and GPUs.

The eFabric™ ecosystem is built upon a radical departure from traditional computing: Silicon-Native Neural Processing. This section explores the physical architecture of the Syntiant® Neural Decision Processor (NDP) and the TML120 module, explaining how we achieve 1000x the efficiency of traditional microcontrollers.

The TML120 module serves as the self-contained "Edge Brain," integrating the NDP silicon with on-board flash memory and high-speed digital interfaces (I2S, PDM, SPI).

Unlike traditional platforms that rely on a software interpreter—which forces a CPU to constantly "wake up" and move data across a bus—eFabric™ leverages Silicon-Native Execution. This allows the weights and architecture of your model to be mapped directly into the hardware gates, enabling a state of "At-Memory Computing." The result is a system that remains in a near-permanent state of deep inference with zero software overhead, providing the foundation for "Always-On" capabilities that allow devices to operate for years on a single coin-cell battery.