Neuromorphic Radio Encoding for Low Power Communication
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Solution Overview
Problem
Current technologies require neuromorphic systems to exit the neuromorphic domain for communication, leading to complications, delays, and loss of low power operation benefits when transmitting neural activity patterns, especially in scenarios like AI agent communication and distributed neural networks.
Innovation Solution
A method using radio frames and carrier frequencies to transmit high-dimensional vectors representing neural activity patterns, where each symbol in the vector selects a subframe or carrier frequency based on a defined mapping, and transmits an impulse at a determined time or frequency offset to encode firing events, allowing seamless communication within the neuromorphic domain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If neuromorphic systems use conventional communication methods to transmit neural activity patterns, then communication between devices is enabled, but the systems must exit the neuromorphic domain causing delays and loss of low power operation benefits
Solution Approach 1:
The patent introduces radio frames and carrier frequencies as an intermediary communication medium that bridges neuromorphic devices while preserving their native operation mode. The radio interface acts as a mediator that translates neural activity patterns into radio signals without requiring the neuromorphic systems to exit their low-power computational state, thus enabling communication while minimizing time loss and power consumption.
Solution Approach 2:
The patent changes the parameter space by mapping neural activity patterns (spike times, frequencies) onto radio communication parameters (time offsets, frequency offsets, subframe selections). This parameter transformation allows neuromorphic devices to communicate using their native spike-based encoding while leveraging radio frequency transmission, eliminating the need to convert to conventional digital communication formats and thereby reducing communication delay.
2Loss of information
If neuromorphic systems use conventional communication methods, then data transmission is achieved, but power consumption increases due to exiting neuromorphic mode
Solution Approach 1:
The radio interface serves as a power-efficient intermediary that enables information transmission without forcing neuromorphic devices to switch to high-power conventional communication modes. By maintaining native spike-based encoding throughout the communication process and only translating to radio parameters at the interface layer, the system achieves effective information transmission while preserving the low-power benefits of neuromorphic operation.
Solution Approach 2:
The patent enables continuous neuromorphic computation during communication by allowing spike generation and radio transmission to occur in an integrated manner. The useful action of neural computation continues uninterrupted while simultaneously generating radio signals for communication, eliminating the power-intensive state transitions that would otherwise be required to exit and re-enter neuromorphic mode for each communication event.
3Adaptability or versatility
If high-dimensional vectors are transmitted using traditional encoding methods, then communication protocol compatibility is achieved, but complexity and processing overhead increase
Solution Approach 1:
The patent transforms the encoding approach by changing from conventional digital bit sequences to parameter-based radio signals. High-dimensional vectors are encoded by mapping their components onto continuous parameters such as time offsets within subframes and frequency offsets from carrier frequencies. This parameter-based encoding reduces complexity by leveraging the natural continuous nature of radio signals while maintaining adaptability to different communication protocols through flexible parameter selection.
Solution Approach 2:
The patent adds a temporal and spectral dimension to the encoding process by utilizing time offsets and frequency offsets as additional encoding spaces. Instead of encoding all information in the amplitude or presence of signals, the system distributes information across multiple dimensions (time, frequency, subframe selection), which simplifies the encoding of high-dimensional vectors by distributing the complexity across orthogonal dimensions rather than concentrating it in a single domain.
Data Source
AI summary
A method by a neuromorphic device in a wireless communication network which communicates using radio frames and carrier frequencies. The method includes obtaining a high dimensional (HD) vector containing symbols. At least some symbols have a value indicating a pattern of firing events for associated one or more neurons of a neural network (NN). For each symbol in the HD vector having a nonzero value, selecting a subframe of a radio frame and/or a carrier frequency among a set of carrier frequencies, based on a defined mapping between subframes of the radio frame and/or carrier frequencies of the set and the locations of symbols in the HD vector, determining a time offset relative to the selected subframe and/or a frequency offset relative to the selected carrier frequency.