Integrated Sensing and ML Processing for Local Analog Preprocessing
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Solution Overview
Problem
Conventional edge devices lack the computational capabilities to perform machine learning processing on sensing data, requiring digitalization and transmission to remote devices, which raises privacy concerns, increases energy consumption, and may not support real-time processing.
Innovation Solution
A semiconductor device with integrated sensing and machine learning processing capabilities, featuring a sensing module that generates analog sensing signals and a machine learning processor that includes crossbar arrays for preprocessing, an analog-to-digital converter, and a processing unit for machine learning operations, all fabricated on a single wafer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sensing data is digitalized and transmitted to remote computing devices for machine learning processing, then machine learning processing capability is improved, but energy consumption increases and real-time processing capability deteriorates
Solution Approach 1:
The patent combines the sensing module and machine learning processing unit into a single integrated device. The sensing module generates analog sensing signals that are directly processed by the crossbar arrays in the processing unit without external digitalization or transmission, enabling local machine learning processing while reducing energy consumption.
Solution Approach 2:
The patent replaces conventional digital signal processing with analog computing using crossbar arrays. The analog sensing signals are directly processed through resistive crossbar structures that perform machine learning operations in the analog domain, eliminating the need for analog-to-digital conversion and reducing energy consumption.
2Adaptability or versatility
If sensing data is transmitted to remote devices, then machine learning processing capability is improved, but data transmission time increases
Solution Approach 1:
The sensing module and processing unit are integrated into a single device, eliminating the need for data transmission to remote devices. The processing unit is located at the same physical location as the sensing module, enabling immediate local processing of sensing data.
Solution Approach 2:
The crossbar arrays are pre-configured with resistive values that encode machine learning model parameters. This preliminary configuration allows the system to perform machine learning operations directly on incoming analog sensing signals without requiring data transmission or external processing.
3Measurement precision
If raw sensing data is transmitted for processing, then processing accuracy is maintained, but privacy concerns increase
Solution Approach 1:
The integrated device processes sensing data locally without external transmission. The sensing module and processing unit operate as a self-contained system, ensuring that raw sensing data never leaves the device and remains protected from external access or interception.
4Device complexity
If conventional edge devices are used without integrated sensing and processing, then device simplicity is maintained, but local processing capability is lost
Solution Approach 1:
The patent integrates the sensing module and machine learning processing unit into a single compact device. This combination provides local processing capability while maintaining relative simplicity through the use of crossbar arrays that perform complex operations with simple resistive structures.
Solution Approach 2:
The patent uses analog crossbar arrays to replace complex digital processing circuits. The resistive crossbar structures perform machine learning operations through passive electrical components, simplifying the device architecture while enabling advanced local processing capabilities.
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
Enables local processing of sensing data, reducing the need for data transmission, enhancing privacy, and supporting real-time processing, while also enabling significant data reduction through preprocessing in the analog domain.
Implementation Method 1
a sensing module configured to generate a plurality of analog sensing signals
Implementation Method 2
one or more crossbar arrays configured to process the analog sensing signals to generate analog preprocessed sensing data
Implementation Method 3
an analog-to-digital converter (ADC) configured to convert the analog preprocessed sensing data into digital preprocessed sensing data
Data Source
AI summary
The present disclosure provides for a semiconductor device with integrated sensing and processing functionalities. The semiconductor device includes a sensing module configured to generate a plurality of analog sensing signals; and a machine learning (ML) processor. The sensing module and the ML processor are fabricated on a single wafer. The ML processor includes crossbar arrays that processes the analog sensing signals to generate analog preprocessed sensing data; an analog-to-digital converter (ADC) to convert the analog preprocessed sensing data into digital preprocessed sensing data; and a machine learning processing unit to process the digital preprocessed sensing data utilizing one or more machine learning model.