Low-Precision AI Processor With 4-Bit Systolic Array Quantization
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Solution Overview
Problem
Conventional processors face inefficiencies in handling the complex algorithms and data-intensive workflows of AI processing due to their architecture and instruction sets optimized for traditional computing tasks, failing to meet the demands of AI workloads efficiently.
Innovation Solution
An AI-accelerating processor with a systolic array where at least 90% of multipliers are limited to 4-bit or lower input precision, combined with memory for storing values in FP16 or higher precision, to perform machine learning operations efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional processors are used for AI tasks, then general-purpose computation capability is maintained, but computational efficiency and power consumption are suboptimal
Solution Approach 1:
The processor is segmented into specialized components: a systolic array for parallel matrix operations, quantization units for precision conversion, and separate memory structures for different precision formats. This segmentation allows each component to be optimized for its specific function, dramatically improving computational efficiency for AI workloads while reducing overall power consumption through targeted optimization rather than general-purpose design.
Solution Approach 2:
The system dynamically changes precision parameters by converting high-precision floating-point values to low-precision quantized values during computation. The quantization unit transforms FP16/FP32 data into integer formats (INT8, INT4, INT2), allowing the systolic array to perform computations at lower precision with reduced power consumption while maintaining sufficient accuracy for AI inference tasks.
2Measurement precision
If high-precision processing is used throughout, then computational accuracy is maintained, but processing speed and efficiency decrease
Solution Approach 1:
Different precision levels are applied to different parts of the computational pipeline based on local requirements. The input memory maintains high precision (FP16/FP32) for accurate data storage, the quantization unit converts to low precision (INT8/INT4/INT2) for fast computation in the systolic array, and results are processed accordingly. This local quality approach ensures accuracy where needed while maximizing speed where possible.
Solution Approach 2:
The precision format is made dynamic rather than static. The system can switch between high-precision and low-precision modes depending on the computational stage and requirements. Data flows through the pipeline with precision being adjusted dynamically - high precision for input/storage, low precision for computation, enabling both accuracy and speed optimization.
3Productivity
If low-precision processing is used, then power consumption is reduced and speed is improved, but computational precision may be insufficient
Solution Approach 1:
Quantization is performed as a preliminary action before computation. The quantization unit pre-processes high-precision input data by converting it to low-precision quantized values that are then fed into the systolic array. This preliminary conversion enables the computation stage to operate at full speed with low power consumption while the quantization process itself ensures that precision requirements are met through proper quantization algorithms and scaling.
Data Source
AI summary
A system may include an integrated circuit for an artificial-intelligence-accelerating (AI-accelerating) processor. The system may retrieve a set of values in a first precision format, the set of values associated with a machine learning model. The system may quantize the set of values to generate a set of quantized values in a second precision format. The system may perform computations of the set of quantized values using a systolic array of AI-accelerating processor. In some embodiments, at least 90% of the multipliers in the systolic array are limited to 4-bit or lower input precision.