Embedded Neural Network FPGA for Implantable Device Latency
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Solution Overview
Problem
Implantable medical devices lack the computational and power resources necessary to run complex deep learning algorithms for sustained periods, and existing solutions do not effectively address the challenges of integrating deep learning into these devices due to limited memory, computational power, and differing operating latencies and data transfer rates among components.
Innovation Solution
The implementation of an embedded networked deep learning (ENDL) system that includes a deep learning neural network, such as a convolutional neural network (CNN), on a field programmable gate array (FPGA) within an implantable medical device, which enables classification of health-related physiological signals and provides early prediction of critical events, using a wireless ultrasonic interface for communication with external devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning algorithms are implemented on implantable medical devices, then classification accuracy and real-time prediction capability are improved, but device complexity and resource requirements increase
Solution Approach 1:
The patent replaces traditional CPU-based deep learning computation with an FPGA-based hardware implementation. This substitution of computational architecture enables parallel processing of neural network operations, achieving >80% classification accuracy while consuming significantly less power and reducing device complexity compared to software-based approaches on resource-constrained implantable devices.
Solution Approach 2:
The patent segments the deep learning processing into distinct hardware modules within the FPGA, including separate units for convolution operations, activation functions, and pooling. This modular segmentation allows efficient resource utilization and reduces overall device complexity by dedicating specific hardware resources to specific computational tasks.
2Productivity
If complex deep learning algorithms are run on implantable devices, then real-time processing capability is improved, but power consumption increases
Solution Approach 1:
The patent replaces power-intensive CPU-based deep learning computation with FPGA hardware acceleration. The FPGA implementation achieves real-time processing of physiological signals with significantly reduced power consumption by utilizing dedicated hardware circuits for parallel computation, eliminating the need for continuous CPU operation and associated power overhead.
Solution Approach 2:
The patent implements periodic processing where the FPGA-based deep learning system processes physiological signals in fixed time intervals rather than continuously. This periodic action reduces power consumption by activating computation only when new data is available, while still maintaining real-time processing capability for critical health monitoring applications.
3Loss of time
If deep learning processing is performed locally on implantable devices, then latency is reduced, but memory and computational resources must be increased
Solution Approach 1:
The patent replaces software-based deep learning on resource-constrained devices with FPGA hardware implementation. This substitution reduces processing latency to minimal hardware propagation delays while requiring only small amounts of on-chip memory for storing neural network weights and intermediate activations, achieving local real-time processing without significant resource increases.
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
The ENDL system achieves classification accuracy greater than 80% with significantly reduced latency and energy consumption compared to CPU-based and cloud-based methods, leading to improved battery lifetime and real-time health event prediction capabilities.
Implementation Method 1
The communication interface comprises an ultrasonic transceiver to transmit and receive ultrasonic signals through biological tissue to and from an external device
Data Source
AI summary
A deep learning medical device implantable in a body is provided. The device includes a processing and communication unit and a sensing and actuation unit. The processing and communication unit includes a deep learning module including a neural network trained to process the input samples, received from the sensing and actuation unit, through a plurality of layers to classify physiological parameters and provide classification results. A communication interface in communication with the deep learning module receives the classification results for ultrasonic transmission through biological tissue. Methods of sensing and classifying physiological parameters of a body and methods of embedding deep learning into an implantable medical device are also provided.