Reconfigurable Systolic Array for Parallel Computing Efficiency
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Solution Overview
Problem
Existing hardware architectures struggle to efficiently support the increasing computational demands of complex learned parameter systems in parallel computing applications, such as deep learning AI, due to inefficiencies in performing multiple operations simultaneously.
Innovation Solution
A reconfigurable systolic array architecture that allows multiple functions to be performed per cycle or configuration, with each row of processing elements capable of executing single, global, or SIMD operations, and utilizing control logic to dynamically route data and configure operations, enabling flexible resource allocation without sacrificing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing hardware architecture is used, then device complexity is reduced, but computation efficiency for complex learned parameter systems deteriorates
Solution Approach 1:
The systolic array architecture implements multi-functionality by enabling each processing element to perform different operations (multiply-accumulate, add, subtract, bitwise operations) based on configurable control signals. The array can be reconfigured to support various algorithms including matrix multiplication, convolution, and neural network computations, allowing a single hardware structure to handle multiple computational tasks efficiently without requiring separate dedicated circuits for each function.
Solution Approach 2:
The patent introduces dynamic reconfigurability where the systolic array can change its operational configuration during execution. Control logic dynamically adjusts the data flow paths, operation types, and processing element assignments based on the specific algorithm being executed. This dynamic adaptation enables the hardware to optimize computation efficiency for different learned parameter systems without physical rehardware changes, resolving the contradiction between hardware simplicity and computational versatility.
2Productivity
If the number of processing elements is increased, then computation capability is improved, but area consumption increases
Solution Approach 1:
The systolic array divides the computational task into segments distributed across multiple processing elements arranged in a grid. Each processing element handles a specific portion of the computation (e.g., one element per neuron in a neural network layer), allowing the system to scale computation capability by adding elements in a structured, space-efficient manner rather than requiring a monolithic increase in hardware area.
Solution Approach 2:
By implementing multi-functional processing elements that can perform various operations (multiply-accumulate, add, subtract, bitwise) through configurable control signals, the patent maximizes the computational output per hardware element. This universality reduces the need for redundant specialized circuits, thereby achieving high computation capability with minimized area consumption compared to dedicated fixed-function architectures.
3Adaptability or versatility
If fixed-function architecture is used, then computation efficiency for specific tasks is improved, but adaptability to different algorithms deteriorates
Solution Approach 1:
The systolic array implements universality through configurable processing elements that can execute multiple operation types (multiply-accumulate, add, subtract, bitwise) based on control signals. This allows the same hardware architecture to adapt to different algorithms including matrix multiplication, convolution, and neural network computations, providing both versatility across algorithms and high computation efficiency through optimized data flow and parallel processing.
Solution Approach 2:
The patent utilizes parameter changes in the control logic to reconfigure the systolic array for different algorithms. By changing control parameters such as data flow directions, operation types, and processing element assignments, the system can efficiently adapt to various computational tasks without hardware modifications. This parameter-based reconfiguration maintains high computation efficiency while achieving broad algorithm adaptability.
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AI summary
Systems and techniques are provided for hardware architecture used in parallel computing applications to improve computation efficiency. An integrated circuit system may include a data store that stores data for processing and a reconfigurable systolic array that may process the data. The reconfigurable systolic array may include a first row of processing elements (PE) that process the data according to a first function and a second row of PE that process the data according to a second function. The reconfigurable systolic array may also include a routing block coupled to the first row of PE, the second row of PE, and the data store. Further, the reconfigurable systolic array may receive data from the first row of PE, transmit the data received from the first row of PE to the second row of PE, and transmit data output by the second row of PE to the first row of PE.