Mixed-Signal Neuron Calibration for Low-Power Accuracy

Resolve Bottlenecks,
Find Innovative Solutions
Generate Solutions

Solution Overview

Problem

Analog neurons in artificial neural networks suffer from impairments such as offset due to device mismatch in the manufacturing process, limiting their accuracy and power efficiency, while digital neurons are high in power consumption.

Innovation Solution

A mixed-signal neuron architecture that includes gain cells with voltage-to-current converters, multipliers for conditionally inverting currents, and a load to convert global currents into output voltages, along with a calibration mechanism to correct offset using a digital-to-analog converter and finite state machine for zero-forcing.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Use of energy by moving object

If analog neurons are used, then power consumption is reduced, but accuracy deteriorates due to offset from device mismatch

Engineering Contradiction:
Improvepower consumptionVSAvoidaccuracy
Core Design Contradiction:
Use of energy by moving objectVSMeasurement precision

Solution Approach 1:

The patent applies preliminary action by performing offset calibration before the neuron operates in its functional mode. A calibration circuit generates calibration currents that pre-compensate for offset errors, so when the neuron processes actual signals, the offset has already been corrected. This allows the neuron to maintain high accuracy while operating in power-efficient analog mode throughout computation.

Inventive Principle:
Principle #10Preliminary action

Solution Approach 2:

The patent introduces an intermediary calibration circuit that mediates between the analog neuron core and the digital control system. This calibration circuit generates corrective calibration currents that are injected into the neuron, acting as an intermediary signal that compensates for offset errors without requiring the entire neuron to operate in high-power digital mode.

Inventive Principle:
Principle #24Intermediary (Mediator)

2Measurement precision

If digital neurons are used, then accuracy is improved, but power consumption increases due to large quantity of transistors

Engineering Contradiction:
ImproveaccuracyVSAvoidpower consumption
Core Design Contradiction:
Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The patent segments the neuron operation into two distinct phases: a brief calibration phase that uses digital-to-analog converters for high accuracy, and a sustained computation phase that uses pure analog circuitry for low power consumption. This segmentation allows the system to get accuracy when needed and power efficiency during extended operation.

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

The patent implements periodic action by performing offset calibration only once or occasionally, rather than continuously. After the initial calibration establishes the correct operating point, the neuron can operate in analog mode without requiring continuous digital intervention, achieving power efficiency during the periodic intervals between calibrations.

Inventive Principle:
Principle #19Periodic action

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

This approach enhances the accuracy and power efficiency of analog neurons by mitigating offset and maintaining robustness, allowing for versatile neuromorphic computing with reduced power consumption.

Implementation Method 1

each respective gain cell in said set of gain cells comprises a respective set of voltage-to-current converters configured to convert a respective input voltage associated with said respective gain cell into a respective set of interim currents

Methodology Applied
Scientific EffectVoltage-to-current conversion: Conduction (electrical)

Implementation Method 2

a load configured to convert the global current into an output voltage

Methodology Applied
Scientific EffectCurrent-to-voltage conversion: Conduction (electrical)

Data Source

PatentUS12147889B2Mixed-signal neurons for neuromorphic computing and method thereof
Publication Date: 2024.11.19 REALTEK SEMICON CORP
  • US12147889B2 patent drawing
  • US12147889B2 patent drawing
  • US12147889B2 patent drawing

AI summary

An artificial neural network and method are provided. The method includes receiving a set of input voltages; converting a respective input voltage in said set of input voltages into a respective set of local currents using a voltage-to-current conversion; multiplying said respective set of local currents by a respective set of binary signals to establish a respective set of conditionally inverted currents; summing said respective set of conditionally inverted currents into a respective local current; summing all respective local currents into a global current; and converting the global current into an output voltage using a load circuit.