Heterogeneous AI Model Speculative Decoding for On-Device LLMs
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Solution Overview
Problem
The high computational cost and energy consumption of large language models (LLMs) on high-performance GPUs pose a barrier to their widespread deployment due to resource constraints and energy demands, necessitating a more efficient on-device processing solution.
Innovation Solution
A federated operation system utilizing speculative decoding, where a lower performance model generates initial token candidates, and a higher performance model validates and accepts or rejects these candidates, optimizing token generation speed and accuracy using heterogeneous language models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large language models are deployed on high-performance GPUs, then response quality and accuracy are improved, but energy consumption and computational cost increase significantly
Solution Approach 1:
The system segments the language model processing into two distinct parts: a smaller first language model that generates candidate tokens, and a larger second language model that validates and selects the final tokens. This segmentation allows the system to distribute computational workload, using the smaller model for high-volume candidate generation and the larger model for quality control, thereby reducing overall energy consumption while maintaining response quality.
Solution Approach 2:
The first language model acts as an intermediary between the input query and the second language model. Instead of directly processing queries through the energy-intensive second model, the system introduces the first model as an intermediate step that pre-processes and filters candidates, reducing the computational burden on the second model and lowering total energy consumption.
2Measurement precision
If larger language models with more parameters are used, then response accuracy is improved, but processing speed decreases due to higher computational complexity
Solution Approach 1:
The system divides the processing task into two segments: the first language model (smaller, faster) generates multiple candidate tokens quickly, while the second language model (larger, more accurate) validates these candidates. This segmentation allows the system to leverage the speed of smaller models for candidate generation and the accuracy of larger models for final selection, achieving both high processing speed and high response accuracy.
Solution Approach 2:
The first language model performs partial action by generating more candidate tokens than will ultimately be needed. This excessive generation of candidates allows the second model to select from a larger pool, improving the likelihood of finding high-quality tokens without requiring the second model to generate all candidates from scratch, thus maintaining processing speed while improving accuracy.
3Use of energy by moving object
If smaller language models are deployed on resource-constrained devices, then energy consumption is reduced, but response quality and accuracy deteriorate
Solution Approach 1:
The system merges the capabilities of two different language models into a unified processing pipeline. The first model (smaller, energy-efficient) handles candidate generation, while the second model (larger, higher quality) handles validation and final selection. This merging allows the system to achieve the energy efficiency of smaller models while maintaining the response quality of larger models, effectively combining the advantages of both.
Solution Approach 2:
The first language model serves as an intermediary that bridges the gap between resource constraints and quality requirements. It pre-processes queries and generates candidates that are then refined by the second model, allowing the system to operate within energy constraints while still achieving high response quality through the collaborative effort of both models.
4Power
If high-performance GPUs are used for language model processing, then computational power is sufficient, but device complexity and hardware requirements increase
Solution Approach 1:
The system segments the computational requirements into two parts: the first language model can be deployed on resource-constrained devices with modest hardware, while the second language model can be deployed on more powerful servers or cloud infrastructure. This segmentation allows flexible deployment architectures where the smaller model handles local processing and the larger model provides enhanced capabilities when available, reducing the need for every device to have high-performance GPUs.
Solution Approach 2:
The first language model serves multiple functions: it processes queries locally on resource-constrained devices, generates candidate tokens, and prepares data for potential second model validation. This multi-functionality allows the system to operate effectively across a range of hardware configurations, from mobile devices to powerful servers, without requiring uniform high-performance hardware throughout the system.
Data Source
AI summary
A device includes a memory of a capacity to store a generative neural network model with first parameters. The device also includes a neural processing unit that generates a response corresponding to an input query utilizing the generative neural network model stored in the memory. The neural processing unit may store execution code of the generative neural network model compiled to process speculative decoding.