Analog AI Signal Processing Architecture Without ADC Conversion
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Solution Overview
Problem
Traditional digital signal processing requires numerous devices, increasing system complexity and cost, and introduces precision loss and processing delay due to signal conversions between analog and digital domains, affecting real-time performance.
Innovation Solution
An analog AI computing architecture comprising a signal obtaining module, filtering module, and computing module that processes analog signals directly, utilizing a sample-and-hold circuit, bandpass filter, and arithmetic unit to perform sampling, filtering, and computation, mimicking the brain's neural processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital signal processing is used to process analog signals, then the signal can be processed with high precision, but the system complexity and cost increase due to requiring numerous devices
Solution Approach 1:
The patent replaces the mechanical/digital signal processing system with a biological neural network system. The analog signals are directly processed by neural networks that mimic biological neuron behavior, eliminating the need for complex digital signal processing chains including ADCs, digital filters, and multiple processing stages. This substitution of mechanical systems with biological-inspired systems resolves the contradiction by maintaining signal integrity while reducing device complexity.
2Productivity
If signal conversion between analog and digital domains is performed, then the signal can be processed digitally, but precision loss occurs during conversion
Solution Approach 1:
The patent eliminates the analog-to-digital conversion process by replacing it with a biological neural network that directly processes analog signals. The neural network neurons receive analog input signals and process them through biological-inspired mechanisms without requiring digital representation, thus avoiding quantization errors and precision loss inherent in ADC conversion.
3Productivity
If analog to digital conversion and back conversion is performed, then the signal can be processed, but additional processing delay is introduced affecting real-time performance
Solution Approach 1:
The patent replaces the multi-stage digital processing system with direct analog processing through neural networks. By eliminating ADC and DAC conversion stages, the system removes the inherent conversion delays and enables real-time signal processing that matches the temporal characteristics of the original analog signal, thus resolving the time loss contradiction.
4Productivity
If numerous devices are used for digital signal processing, then the processing capability is enhanced, but power consumption increases
Solution Approach 1:
The patent substitutes energy-intensive digital processing devices with a biological-inspired neural network system that processes analog signals directly. The neural network architecture, inspired by biological neurons, achieves comparable or superior processing capability with significantly lower power consumption by avoiding the high-energy operations of digital signal processing chains.
Data Source
AI summary
The present invention provides an analog AI computing architecture, and relates to the technical field of circuit design. The architecture includes: a signal obtaining module, configured to sample an analog signal to obtain signal sample data; a filtering module, configured to perform bandpass filtering on the signal sample data by an AI signal transmission mechanism to obtain filtered data; and a computing module, configured to compute the filtered data by an arithmetic unit and an external control switch to obtain a processed analog signal. The present invention simplifies the processing flow of traditional digital signals, improves the computing speed, and reduces the power consumption and system complexity required for computing.