Symmetric Matrix Processing Element for AI Forward and Backpropagation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current processing technologies, such as GPUs and FPGAs, face inefficiencies in performing complex matrix operations required for artificial intelligence tasks, particularly due to latency issues and limitations in handling both forward and backpropagation directions without reloading data, which hampers real-time computational performance.
Innovation Solution
A Symmetric Matrix Processing Element is designed to efficiently read, write, and process matrix data in parallel, enabling rapid forward and backpropagation calculations without reloading weight matrices, utilizing a wide SRAM for simultaneous access and operation of entire rows, and a control system to manage data flow through MAC units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional Von Neumann architecture is used for matrix operations, then flexibility in performing any calculation is maintained, but computational time becomes extremely long due to sequential processing iterations
Solution Approach 1:
The processing element is segmented into specialized functional units including multiple MAC units arranged in a grid pattern, each capable of independent parallel operation. This segmentation allows simultaneous execution of multiple matrix operations that would otherwise require sequential processing, dramatically reducing computational time while maintaining calculation flexibility through configurable data flow paths.
2Productivity
If data is repeatedly loaded from memory for each processing iteration, then processing can be performed, but the time required becomes very long due to memory access latency
Solution Approach 1:
Weight matrices are pre-loaded into local memory structures within the processing element before computation begins. The architecture includes on-chip memory buffers that hold weight data throughout the forward and backward propagation processes, eliminating the need for repeated memory accesses during processing iterations and significantly reducing memory access time.
3Speed
If specialized processors like GPUs are used for matrix operations, then processing speed is improved, but it remains difficult to perform complex AI calculations efficiently due to data reloading requirements
Solution Approach 1:
The processing element maintains continuous computation by keeping weight matrices resident in local memory throughout both forward propagation and backward propagation phases. Data flows continuously through the MAC units without interruption for reloading, enabling uninterrupted computational sequences that maximize processing speed while eliminating idle time associated with data reloading.
4Ease of manufacture
If weight matrices are stored in standard memory configuration, then storage is simple, but efficient simultaneous access to entire rows for parallel processing is not achieved
Solution Approach 1:
The memory architecture implements a two-dimensional array organization for weight matrices, where weights are arranged in rows and columns corresponding to their mathematical indices. This dimensional organization allows simultaneous access to entire rows or columns through row-column addressing mechanisms, enabling parallel MAC units to efficiently retrieve multiple weight values in a single operation cycle without complicating the fundamental memory structure.
Data Source
AI summary
Artificial intelligence is an increasingly important sector of the computer industry. However, artificial intelligence is very computationally intensive field. Fortunately, many of the required calculations can be performed in parallel such that specialized processors can greatly increase computation performance. In particular, Graphics Processor Units (GPUs) are often used in artificial intelligence. Although GPUs have helped, they are not ideal for artificial intelligence. Specifically, GPUs are used to compute matrix operations in one direction with a pipelined architecture. However, artificial intelligence is a field that uses both forward propagation computations and back propagation calculations. To efficiently perform artificial intelligence calculations, a symmetric matrix processing element is introduced. The symmetric matrix processing element can perform forward propagation and backward propagation calculations just as easily. Furthermore, both of these calculations can be performed without reloading weight matrix values.