arxiv_ml 95% Match Research Paper ML engineers,Robotics engineers,Mobile app developers,Researchers in efficient AI 1 week ago

RWKV-edge: Deeply Compressed RWKV for Resource-Constrained Devices

large-language-models › model-architecture

📄 Abstract

Abstract: To deploy LLMs on resource-contained platforms such as mobile robots and smartphones, non-transformers LLMs have achieved major breakthroughs. Recently, a novel RNN-based LLM family, Repentance Weighted Key Value (RWKV) has shown strong computational efficiency; nevertheless, RWKV models still have high parameter counts which limited their deployment. In this paper, we propose a suite of compression techniques, ranging from model architecture optimizations to post-training compression, tailored to the RWKV architecture. Combined, our techniques reduce the memory footprint of RWKV models by 3.4x -- 5x with only negligible degradation in accuracy; compared to transformer LLMs with similar accuracy, our models require 4x less memory footprint.

Authors (3)

Wonkyo Choe

Yangfeng Ji

Felix Xiaozhu Lin

Submitted

December 14, 2024

arXiv Category

cs.LG

arXiv PDF

Key Contributions

This paper introduces a suite of compression techniques specifically tailored for the RWKV architecture, significantly reducing its memory footprint (3.4x-5x) with negligible accuracy degradation. This enables the deployment of powerful LLMs on resource-constrained devices like smartphones and mobile robots, outperforming transformers in memory efficiency for similar accuracy.

Business Value

Enables the integration of advanced AI capabilities, like natural language understanding and generation, into a wider range of consumer electronics and embedded systems, driving innovation in mobile applications and robotics.

Paper Metadata

Innovation Type

Algorithmic

Deployment Feasibility

High, as the techniques are designed for practical deployment on existing hardware with limited resources.

Limitations Addressed

High parameter counts of RWKV models,Limited deployment of LLMs on resource-constrained platforms,High memory footprint of LLMs

Performance Gains

3.4x-5x reduction in memory footprint for RWKV models,4x less memory footprint compared to transformers with similar accuracy

Technical Tags

LLM compressionRWKVRNNresource-constrained devicesmodel optimizationpost-training compressionmemory footprint reductionparameter count reductionmobile robotssmartphones

Research Topics

Large Language ModelsModel CompressionEfficient AIEdge ComputingRecurrent Neural Networks

Methods & Architectures

Model architecture optimizationsPost-training compressionParameter quantizationWeight pruning RWKVRNNTransformer (for comparison)

Applications & Tasks

Edge AI Mobile Devices Robotics Internet of Things (IoT) Model CompressionReducing computational costReducing memory footprintDeploying LLMs on edge devices Deploying LLMs on resource-constrained devicesEnabling LLM capabilities on mobile robots and smartphones

Datasets & Benchmarks

Benchmarks

Memory footprint reduction: 3.4x - 5x • Memory footprint reduction compared to transformers: 4x less

AccuracyMemory footprintParameter count

Related Fields

Natural Language ProcessingEdge ComputingMobile AIEmbedded SystemsComputer Architecture

Keywords

LLM compressionRWKVRNNedge AIresource-constrainedmodel optimizationmemory footprintparameter reductionmobile robotssmartphonesefficient AIdeep learning

Academic Context

#Large Language Models#Model Compression#Efficient AI#Edge Computing#Recurrent Neural Networks

Commercial Potential

Potential Products

On-device AI assistantsSmartphones with advanced NLP capabilitiesRobots with enhanced conversational abilitiesIoT devices with local intelligence

Target Industries

Consumer ElectronicsAutomotiveRoboticsHealthcare (wearables)

Use Case Examples

Voice assistants on smartphones without cloud dependencyRobots that can understand and respond to complex commands locallySmart home devices with embedded AI

Competitive Edge

Provides a highly efficient alternative to transformer-based LLMs for edge deployment, specifically optimizing the RWKV architecture.

Market Opportunity

The market for edge AI devices and on-device LLM applications is rapidly expanding.

Revenue Models

Enabling new product featuresreducing cloud inference costs for companies.

Resource Requirements

Compute Needs

Significantly reduced compared to standard LLMs, suitable for mobile CPUs/GPUs and embedded processors.

Data Requirements

Standard LLM training datasets for initial model development; compression techniques are applied post-training or during fine-tuning.

Deployment Constraints

Limited on-device memory,Limited processing power,Battery life considerations

Scalability

The compression techniques themselves are scalable, allowing for efficient application to various RWKV model sizes.

Regulatory Considerations

Data privacy for on-device processing

Production Readiness

Maturity Level

Research

Time to Market

6-18 months for integration into consumer products.

Patent Potential

Moderate, for novel compression algorithms or specific architectural optimizations.

View Full Paper Back to Papers