Analog Inference Engine With Integrated Weight Updating
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Solution Overview
Problem
Conventional neural network-based signal processing systems are power-hungry and require significant computational resources, making them impractical for edge applications, and updating neural network weights is inefficient in analog inference engines.
Innovation Solution
A signal processing system incorporating an analog inference engine and a learning engine, where the analog inference engine applies weights to input signals and the learning engine updates these weights using forward passes and error diffusion techniques, allowing for efficient weight updating and reduced power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital inference engines are used for neural network processing, then computational accuracy is maintained, but power consumption and computational resource requirements increase significantly
Solution Approach 1:
The patent replaces digital computational systems with analog neural network circuits that use continuous electrical signals to perform neural network operations. The analog inference engine uses physical circuit elements (transistors, resistors, capacitors) arranged in neural network topologies to compute activations and weights through voltage and current flows, eliminating the need for digital processors and significantly reducing power consumption while maintaining computational accuracy for signal processing tasks
2Reliability
If separate learning engines are added to update weights, then neural network accuracy is maintained, but device complexity and computational resource requirements increase
Solution Approach 1:
The patent merges the learning and inference functions into a single integrated analog neural network system. The same analog circuitry that performs inference also performs learning through weight updates, eliminating the need for separate learning engines. Weight updates are performed by adjusting the physical values of circuit elements (resistor, transistor thresholds) directly in the analog domain, allowing the system to maintain accuracy while reducing overall system complexity
Solution Approach 2:
The analog neural network circuit is designed to perform multiple functions: it serves as both the inference engine for real-time signal processing and the learning engine for weight adaptation. The same physical circuit structure can be configured to perform forward propagation, backpropagation, and weight updates by controlling the state of its components, making the system universally capable of both inference and learning operations without requiring separate dedicated hardware
3Productivity
If conventional digital systems are used for edge applications, then computational capability is sufficient, but power consumption makes deployment impractical
Solution Approach 1:
The patent substitutes digital computational systems with analog neural network circuits that perform signal processing operations using continuous electrical signals. The analog inference engine processes input signals through voltage and current flows in neural network topologies, achieving the required computational capability for edge applications while consuming significantly less power than digital systems, making deployment in battery-powered edge devices practical
Data Source
AI summary
A signal processing system configured to receive an input signal and generate an output signal, the signal processing system comprising: an analog inference engine; and a learning engine coupled to the analog inference engine, wherein the signal processing system is operable in a first mode of operation and a second mode of operation, wherein: in the first mode of operation, the analog inference engine is operative to apply weights to the received input signal to generate the output signal; and in the second mode of operation, the analog inference engine is operative to receive a test input signal and process the test input signal to generate a test output signal, and the learning engine is operative to update the weights of the inference engine based on the test output signal.