Digital Camera Deep Learning Accelerator Reducing Data Volume
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Digital cameras face challenges in efficiently processing and storing large amounts of image data, leading to high energy consumption and increased bandwidth requirements for communication and storage.
Innovation Solution
Integration of a Deep Learning Accelerator (DLA) and random access memory in digital cameras, which enables local processing of image data using Artificial Neural Networks (ANNs) to generate intelligent outputs, reducing the need to store or transmit raw image data by converting it into compact inference results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If image data is processed and stored using conventional methods, then data storage capacity and communication bandwidth requirements increase, but energy consumption and system complexity are reduced
Solution Approach 1:
The patent extracts only the essential information from raw image data through ANN processing. Instead of storing or transmitting complete image datasets, the system processes images through trained neural networks to extract key features, objects, or events, storing only these extracted results which occupy minimal storage space and require minimal transmission bandwidth.
Solution Approach 2:
The patent implements preliminary processing of image data within the camera device itself using integrated ANN accelerators. Image data is processed locally to generate extracted results before any potential transmission or storage, eliminating the need for energy-consuming post-processing operations and reducing the data volume that would otherwise need to be handled.
2Adaptability or versatility
If raw image data is transmitted and stored externally, then processing flexibility is maintained, but bandwidth requirements and privacy risks increase
Solution Approach 1:
The system extracts only necessary information from images while leaving the original data localized. By processing images through ANNs to extract specific features, objects, or events of interest, the patent maintains processing flexibility for those extracted results while preventing exposure of complete raw image data, thereby protecting privacy without sacrificing analytical versatility.
Solution Approach 2:
The patent introduces an intermediary processing layer (ANN accelerator) between the image sensor and external systems. This intermediary locally processes image data and generates extracted results, acting as a mediator that preserves privacy by not transmitting raw data while maintaining versatility by providing meaningful processed information to external systems.
3Quantity of substance
If deep learning processing is performed locally in the camera, then data transmission requirements are reduced, but device complexity and computational requirements increase
Solution Approach 1:
The patent segments the computational workload by integrating specialized ANN accelerators within the camera device. Instead of requiring a complete deep learning system, the camera contains dedicated hardware modules (such as matrix-vector multiplication units, vector-vector multiplication units, and local memory) that handle specific neural network operations, dividing the overall processing task between the camera and external systems.
Solution Approach 2:
The camera device performs self-processing of image data through integrated ANN accelerators. The local memory stores ANN parameters and intermediate results, and the processing units execute neural network computations autonomously without requiring external computational resources, enabling the device to serve its own processing needs and minimize data transmission.
4Productivity
If conventional image processing is used, then device simplicity is maintained, but processing speed and intelligence capabilities are reduced
Solution Approach 1:
The patent divides the processing system into specialized components: image sensors for data capture, ANN accelerators for intelligent processing, and local memory for parameter storage. This segmentation allows each component to be optimized for its specific function, with processing units dedicated to matrix-vector multiplication, vector-vector multiplication, and activation function computation, achieving high processing efficiency through specialized architecture rather than general-purpose computing.
Solution Approach 2:
The patent replaces conventional mechanical or general-purpose computing approaches with specialized neural network processing architecture. Instead of using traditional image processing algorithms that require complex control logic and multiple processing stages, the system uses ANN-based processing that naturally handles complex patterns and intelligence tasks through parallel computation in the neural network layers.
Data Source
AI summary
Systems, devices, and methods related to a Deep Learning Accelerator and memory are described. For example, a digital camera may be configured to execute instructions with matrix operands and configured with: a housing; a lens; an image sensor positioned behind the lens to generate image data of a field of view of the digital camera; random access memory to store instructions executable by the Deep Learning Accelerator and store matrices of an Artificial Neural Network; a transceiver; and a controller configured to generate, and communicate using the transceiver to a separate computer, a description of an item or event in the field of view captured in the image data, based on an output of the Artificial Neural Network receiving the image data as an input. The separate computer may selectively request a portion of image data from the digital camera based on the processing of the description.