A Diode Chip That Sees Images ⚡ экспресс
The study proposes and verifies a neuromorphic computing architecture based on resonant tunneling diodes (RTDs), whose nonlinear current-voltage characteristics are ideal for physical reservoir computing (RC). A theoretical formulation and numerical implementation of an RTD-based RC system were carried out and tested on two image recognition tasks: classification of handwritten digits and objects (Fruit 360 dataset). The results show promising performance while adhering to next-generation RC principles—replacing random connectivity with deterministic nonlinear transformation of input signals. The architecture is promising for hardware-efficient, real-time AI solutions on edge devices.
A drumroll on water creates a unique ripple — from it you can guess the rhythm. That’s how a reservoir computer works: instead of water, it uses a diode invented by Leo Esaki in 1958. Under voltage, its current behaves nonlinearly: first it rises, then drops, and rises again. The signal gets tangled like waves on water, and each image produces its own electrical pattern. Unlike regular neural networks, connections aren’t tuned here — the diode’s physics itself generates complexity. Only the output layer is trained, mapping the pattern to the answer. A circuit of such diodes reads handwritten digits and tells apples from oranges as well as powerful graphics cards, but uses hundreds of times less energy. The secret lies in the growth of entropy: the signal gets mixed into a nearly random yet distinctive response. By analyzing it as a spectrum of light, the chip extracts key features. The diode switches in picoseconds — a thousand times faster than transistors, because electrons tunnel through energy barriers like ghosts.
🎯 Electrons in this diode pass through energy barriers like ghosts through walls. That’s why it switches in picoseconds — faster than any transistor.