Array Processor with Macro-Instructions for Zero-Latency Data Movement
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Solution Overview
Problem
Existing array processors lack efficient mechanisms for processing complex, multi-dimensional array data with flexible configuration and parallel processing capabilities, leading to suboptimal performance in applications requiring matrix manipulation and other array operations.
Innovation Solution
The development of array processors with a custom instruction set architecture (ISA) utilizing macro-instructions that enable zero-overhead looping and array iteration, combined with a programmable set of macro-instructions that allow for flexible N-dimensional array configuration, parallel input and output buses, and ALU input transformations, facilitating efficient processing of array data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional array processors are used, then basic array processing is possible, but processing efficiency and throughput are insufficient for complex multi-dimensional array operations
Solution Approach 1:
The processor is divided into multiple processing elements (PEs) organized in an array structure, where each PE can independently process array elements. This segmentation enables parallel processing of multi-dimensional arrays, significantly improving productivity while maintaining a modular architecture that manages complexity.
Solution Approach 2:
The processing elements are designed with universal functionality to handle various array operations including matrix multiplication, convolution, and other multi-dimensional array manipulations. This multi-functionality allows a single processor architecture to efficiently process different types of array data without requiring specialized hardware for each operation.
2Speed
If data movement operations are performed in conventional processors, then data can be transferred between memory and processing units, but data movement overhead and latency are excessive
Solution Approach 1:
The memory system and processing elements are merged into a unified architecture where processing elements can directly access memory through integrated data paths. This merging eliminates the need for separate data movement operations between distinct memory and processing units, reducing both overhead and latency.
Solution Approach 2:
Data is pre-loaded into local buffers and registers within processing elements before computation begins. This preliminary action reduces the need for repeated data movement during processing, thereby minimizing latency and overhead for subsequent operations.
3Adaptability or versatility
If flexible configuration is implemented for N-dimensional arrays, then processing adaptability is improved, but control mechanism complexity increases
Solution Approach 1:
The processor incorporates dynamic configuration mechanisms that allow array dimensions, strides, and data types to be adjusted at runtime through programmable control registers. This dynamic adaptability enables flexible handling of N-dimensional arrays without requiring complex hardwired control logic for each configuration scenario.
4Productivity
If parallel processing capabilities are enhanced, then throughput for array operations is improved, but hardware resource requirements increase
Solution Approach 1:
Processing elements are organized in a nested hierarchy where smaller processing units are grouped into larger arrays, which can be further grouped into multi-dimensional structures. This nesting allows the processor to achieve high throughput by utilizing multiple levels of parallelism while efficiently sharing hardware resources across different processing tiers.
Data Source
AI summary
An apparatus includes an array processor to process array data in response to information contained in a packet, wherein the packet comprises a set of fields specifying configuration information for processing the array.