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A General-Purpose Device for Interaction with LLMs

Jiajun Xu, Qun Wang, Yuhang Cao, Baitao Zeng, Sicheng Liu

TL;DR

The paper addresses the hardware-software gap in deploying large language models (LLMs) by proposing a general-purpose edge device designed for robust interaction with LLMs. It introduces a five-component framework (input edge device, LLM controller, third-party APIs, database, and task planning library) and details an edge-centric architecture featuring multimodal sensors, offline wake-word processing, an efficient edge LLM with quantization, and a cloud-edge feedback loop. Key contributions include a comprehensive hardware-software design, an audio-centric input pipeline with VAD/AEC/denoising/de-reverberation followed by ASR, a local caching mechanism for rapid responses, and an LLM-driven controller that orchestrates multi-source data and internet access while preserving privacy. The work demonstrates a pathway toward scalable, private, real-time LLM interactions across home, office, and industrial contexts, highlighting future work in scalable hardware, multimodal processing, personalization, and interoperability to enable widespread adoption of LLM-enabled intelligent devices.

Abstract

This paper investigates integrating large language models (LLMs) with advanced hardware, focusing on developing a general-purpose device designed for enhanced interaction with LLMs. Initially, we analyze the current landscape, where virtual assistants and LLMs are reshaping human-technology interactions, highlighting pivotal advancements and setting the stage for a new era of intelligent hardware. Despite substantial progress in LLM technology, a significant gap exists in hardware development, particularly concerning scalability, efficiency, affordability, and multimodal capabilities. This disparity presents both challenges and opportunities, underscoring the need for hardware that is not only powerful but also versatile and capable of managing the sophisticated demands of modern computation. Our proposed device addresses these needs by emphasizing scalability, multimodal data processing, enhanced user interaction, and privacy considerations, offering a comprehensive platform for LLM integration in various applications.

A General-Purpose Device for Interaction with LLMs

TL;DR

The paper addresses the hardware-software gap in deploying large language models (LLMs) by proposing a general-purpose edge device designed for robust interaction with LLMs. It introduces a five-component framework (input edge device, LLM controller, third-party APIs, database, and task planning library) and details an edge-centric architecture featuring multimodal sensors, offline wake-word processing, an efficient edge LLM with quantization, and a cloud-edge feedback loop. Key contributions include a comprehensive hardware-software design, an audio-centric input pipeline with VAD/AEC/denoising/de-reverberation followed by ASR, a local caching mechanism for rapid responses, and an LLM-driven controller that orchestrates multi-source data and internet access while preserving privacy. The work demonstrates a pathway toward scalable, private, real-time LLM interactions across home, office, and industrial contexts, highlighting future work in scalable hardware, multimodal processing, personalization, and interoperability to enable widespread adoption of LLM-enabled intelligent devices.

Abstract

This paper investigates integrating large language models (LLMs) with advanced hardware, focusing on developing a general-purpose device designed for enhanced interaction with LLMs. Initially, we analyze the current landscape, where virtual assistants and LLMs are reshaping human-technology interactions, highlighting pivotal advancements and setting the stage for a new era of intelligent hardware. Despite substantial progress in LLM technology, a significant gap exists in hardware development, particularly concerning scalability, efficiency, affordability, and multimodal capabilities. This disparity presents both challenges and opportunities, underscoring the need for hardware that is not only powerful but also versatile and capable of managing the sophisticated demands of modern computation. Our proposed device addresses these needs by emphasizing scalability, multimodal data processing, enhanced user interaction, and privacy considerations, offering a comprehensive platform for LLM integration in various applications.
Paper Structure (15 sections, 2 figures)

This paper contains 15 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Framework of Proposed Design
  3. User Needs and Design Goals
  4. Framework Overview
  5. Edge Device Implement
  6. Major Hardware Components for Our Framework
  7. Input Process
  8. Detailed Audio Input Processing
  9. Local Caching
  10. LLM as a Controller
  11. Output Feedback
  12. Future Work and Challenges
  13. Future Work
  14. Overcoming Implementation Challenges
  15. Conclusion

Figures (2)

  • Figure 1: Framework of proposed design.
  • Figure 2: Information process flow from edge to core.