Analog Neural Network Accelerator Parallelization Pipelining
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Solution Overview
Problem
Deep learning and neural network computations, particularly matrix operations, are computationally intensive and require long processing times due to the inefficiencies in existing digital processing systems, especially when dealing with large data sets and weight matrices.
Innovation Solution
A processing system comprising an analog accelerator, such as a photonic accelerator, and a digital processor, where the analog accelerator performs linear operations like matrix multiplications using data or tile parallelism and the digital processor handles non-linear operations, allowing for pipelining techniques to maximize throughput and reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If digital processing systems are used for matrix operations in deep learning, then the system is easy to implement and control, but the processing time is long and computational efficiency is low
Solution Approach 1:
The patent replaces digital electronic processing systems with an optical processing system that uses light to perform matrix multiplication operations. Optical fields are used instead of electrical signals to compute dot products of weight matrices and input data, enabling parallel computation of multiple operations simultaneously and dramatically reducing processing time for computationally intensive deep learning tasks
Solution Approach 2:
The patent segments the weight matrix and input data into multiple tiles or blocks that can be processed in parallel. By dividing the large-scale matrix operations into smaller sub-tasks that can be computed simultaneously using multiple optical channels, the system achieves high computational throughput while maintaining accuracy
2Productivity
If analog accelerator performs linear operations using parallelism and pipelining, then the throughput is improved and processing time is reduced, but the system complexity increases
Solution Approach 1:
The patent implements pipelining where the analog accelerator continuously processes multiple matrix multiplication operations in an overlapping manner. While one matrix multiplication is being computed, the system is already preparing the next set of input data and weight tiles, ensuring the analog accelerator remains fully utilized and maximizing throughput without idle time
Solution Approach 2:
The patent introduces a controller as an intermediary component that manages the complex coordination between digital memory storage and analog computation. The controller handles data formatting, tile generation, and operation scheduling, isolating the complexity from the core analog accelerator and allowing it to focus on high-speed parallel computation
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 significantly reduces processing time for matrix operations, improving the efficiency of deep learning algorithms by keeping the analog accelerator fully utilized and overlapping linear and non-linear operations, thereby enhancing the overall system throughput.
Implementation Method 1
the analog accelerator comprises a photonic accelerator, and wherein controlling the photonic accelerator to perform matrix multiplication using light
Data Source
AI summary
Parallelization and pipelining techniques that can be applied to multi-core analog accelerators are described. The techniques descried herein improve performance of matrix multiplication (e.g., tensor-tensor multiplication, matrix-matrix multiplication or matrix-vector multiplication). The parallelization and pipelining techniques developed by the inventors and described herein focus on maintaining a high utilization of the processing cores. A representative processing systemin includes an analog accelerator, a digital processor, and a controller. The controller is configured to control the analog accelerator to output data using linear operations and to control the digital processor to perform non-linear operations based on the output data.