ML ASIC Encoding Optimization Using FPGA Precision Prototyping
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Solution Overview
Problem
Existing machine learning hardware inefficiencies stem from arbitrary number encoding choices, leading to sub-optimal performance and resource wastage, as modern systems lack optimization for specific computational workloads of large language models.
Innovation Solution
A method involving FPGA prototyping and custom toolchains to determine optimal encoding sizes for machine learning ASICs, coupled with experimental evaluation and degradation analysis, to create optimized Register Transfer Level (RTL) designs tailored for specific models and datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If lower precision encodings are used to reduce memory footprint and improve computation speed, then hardware efficiency and computation speed are improved, but model performance may deteriorate
Solution Approach 1:
The patent systematically varies encoding precision parameters (bit-width, data format) to identify optimal settings that balance computation speed and model performance. Different precision levels (e.g., 8-bit, 16-bit, 32-bit) are tested to find the threshold where performance degradation becomes unacceptable.
Solution Approach 2:
FPGA prototyping is conducted before final ASIC fabrication to pre-determine optimal encoding precision for specific workloads. This preliminary experimentation prevents wasted fabrication cycles and ensures the final design uses the most efficient encoding scheme.
2Productivity
If FPGA prototyping and experimental evaluation are conducted to determine optimal encoding sizes, then hardware efficiency is optimized, but development time and complexity increase
Solution Approach 1:
FPGA serves as an intermediary platform between software simulation and final ASIC fabrication. It allows rapid prototyping and evaluation of different encoding schemes without committing to permanent silicon fabrication, thus reducing overall development risk and complexity.
Solution Approach 2:
The development process is segmented into distinct phases: software simulation, FPGA prototyping, and ASIC fabrication. Each phase has specific objectives and validation criteria, allowing systematic optimization while managing complexity through staged development.
3Ease of manufacture
If standard encoding sizes are used in existing hardware, then implementation simplicity is maintained, but resource wastage and sub-optimal performance occur
Solution Approach 1:
The patent advocates for workload-specific encoding optimization rather than universal standard encodings. Different regions of the hardware or different computational kernels may use different encoding precisions tailored to their specific requirements, maximizing resource utilization efficiency.
Solution Approach 2:
The system dynamically selects encoding precision based on workload characteristics and performance requirements. Rather than fixed standard encodings, the hardware can adaptively adjust precision levels to match the computational demands of different machine learning tasks.
Data Source
AI summary
Disclosed is a design process for high-performance specialized machine learning ASICs, optimized for given models and training or inference hardware end use. Modern Large Language Models (LLMs) and deep learning models can require trillions of parameters to be calculated, and the hardware currently used is not tailored for specific models or input datasets. A key tuneable parameter in custom hardware design is the encoding size of numbers. FPGA prototypes are used to test custom number encoding sizes, which informs the final fabricated design which is created with optimized RTL for the encoding size with attention to number register locations, and component sizes. By first analyzing specific machine learning models on prototype FPGA hardware with variable encoding sizes, the optimal number(s) for encoding size for both training and inference can be identified. By experimentally establishing an optimized encoding sizes for the specific computing use case wasted overhead in terms of physical registers is minimized. The approach herein minimizes research and development costs while optimizing encoding sizes for machine learning ASICS.