Deep Learning Accelerator with Camera Interface for Concurrent Memory Access
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Solution Overview
Problem
Existing integrated circuit devices face challenges in efficiently processing Artificial Neural Networks (ANNs) due to high energy consumption and computation time, particularly when handling large matrix and vector operations, which limits their performance in applications like computer vision and autonomous systems.
Innovation Solution
The integration of a Deep Learning Accelerator (DLA) with random access memory and a camera interface, optimized for parallel vector and matrix calculations, reduces energy consumption and computation time by allowing concurrent memory access and autonomous operation of ANNs, minimizing reliance on Central Processing Units (CPUs).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If conventional integrated circuit devices process ANNs using CPUs, then general-purpose computing flexibility is maintained, but energy consumption and computation time increase significantly
Solution Approach 1:
The system segments processing tasks by separating CPU general-purpose functions from DLA specialized ANN processing functions. The DLA is divided into multiple processing clusters that can independently execute different ANN operations simultaneously, enabling parallel processing while maintaining energy efficiency.
Solution Approach 2:
The DLA acts as an intermediary between the CPU and memory system, handling all ANN processing tasks independently. This mediator architecture allows the CPU to offload computationally intensive ANN operations to the DLA, reducing CPU energy consumption while maintaining system flexibility through standardized interfaces.
2Productivity
If large matrix and vector operations are performed using conventional processors, then ANN computations can be executed, but computation time and energy consumption increase
Solution Approach 1:
The DLA merges multiple processing functions into unified processing clusters that handle matrix operations, vector operations, and activation functions within single computational units. This consolidation eliminates data movement between separate processing stages, reducing both computation time and energy consumption.
Solution Approach 2:
The system transitions from sequential CPU processing to parallel processing across multiple DLA clusters operating simultaneously. This dimensional change in processing architecture enables concurrent execution of multiple ANN layers and operations, dramatically improving computational throughput while maintaining energy efficiency through specialized hardware optimization.
3Ease of operation
If camera data is processed through standard interfaces, then compatibility is maintained, but data traffic to CPU increases
Solution Approach 1:
The system extracts image data processing from the CPU workflow and directs it directly to the DLA through dedicated camera interfaces. This extraction eliminates unnecessary data routing through the CPU, reducing data traffic volume while maintaining processing efficiency through direct DLA access to camera inputs.
Data Source
AI summary
Systems, devices, and methods related to a Deep Learning Accelerator and memory are described. An integrated circuit may be configured to execute instructions with matrix operands and configured with: random access memory configured to store instructions executable by the Deep Learning Accelerator and store matrices of an Artificial Neural Network; a connection between the random access memory and the Deep Learning Accelerator; a first interface to a memory controller of a Central Processing Unit; and a second interface to an image generator, such as a camera. While the Deep Learning Accelerator is using the random access memory to process current input to the Artificial Neural Network in generating current output from the Artificial Neural Network, the Deep Learning Accelerator may concurrently load next input from the camera into the random access memory; and at the same time, the Central Processing Unit may concurrently retrieve prior output from the random access memory.