HADES: Hardware Accelerated Decoding for Efficient Speculation in Large Language Models

• URL: http://arxiv.org/abs/2412.19925v2
• Date: Mon, 13 Jan 2025 04:33:01 GMT
• Title: HADES: Hardware Accelerated Decoding for Efficient Speculation in Large Language Models
• Authors: Ze Yang, Yihong Jin, Xinhe Xu,
• Abstract summary: Large Language Models (LLMs) have revolutionized natural language processing by understanding and generating human-like text. This paper introduces Hardware Accelerated Decoding (HADES), a novel approach to enhance the performance and energy efficiency of LLMs.
• Score: 1.2180334969164464
• License:
• Abstract: Large Language Models (LLMs) have revolutionized natural language processing by understanding and generating human-like text. However, the increasing demand for more sophisticated LLMs presents significant computational challenges due to their scale and complexity. This paper introduces Hardware Accelerated Decoding (HADES), a novel approach to enhance the performance and energy efficiency of LLMs. We address the design of an LLM accelerator with hardware-level speculative decoding support, a concept not previously explored in existing literature. Our work demonstrates how speculative decoding can significantly improve the efficiency of LLM operations, paving the way for more advanced and practical applications of these models.

Related papers

• Exploring Code Language Models for Automated HLS-based Hardware Generation: Benchmark, Infrastructure and Analysis [49.998130983414924]
  Large language models (LLMs) can be employed for programming languages such as Python and C++. This paper explores leveraging LLMs to generate High-Level Synthesis (HLS)-based hardware design.
  arXiv  Detail & Related papers  (2025-02-19T17:53:59Z)
• HiVeGen -- Hierarchical LLM-based Verilog Generation for Scalable Chip Design [55.54477725000291]
  HiVeGen is a hierarchical Verilog generation framework that decomposes generation tasks into hierarchical submodules. automatic Design Space Exploration (DSE) into hierarchy-aware prompt generation, introducing weight-based retrieval to enhance code reuse. Real-time human-computer interaction to lower error-correction cost, significantly improving the quality of generated designs.
  arXiv  Detail & Related papers  (2024-12-06T19:37:53Z)
• EMMA: Efficient Visual Alignment in Multi-Modal LLMs [56.03417732498859]
  EMMA is a lightweight cross-modality module designed to efficiently fuse visual and textual encodings. EMMA boosts performance across multiple tasks by up to 9.3% while significantly improving robustness against hallucinations.
  arXiv  Detail & Related papers  (2024-10-02T23:00:31Z)
• Are LLMs Any Good for High-Level Synthesis? [1.3927943269211591]
  Large Language Models (LLMs) can streamline or replace the High-Level Synthesis (HLS) process. LLMs can understand natural language specifications and translate C code or natural language specifications. This study aims to illuminate the role of LLMs in HLS, identifying promising directions for optimized hardware design in applications such as AI acceleration, embedded systems, and high-performance computing.
  arXiv  Detail & Related papers  (2024-08-19T21:40:28Z)
• LLM-Aided Compilation for Tensor Accelerators [6.709490736813537]
  We discuss how large language models (LLMs) could be leveraged to build a compiler for hardware accelerators. Specifically, we demonstrate the ability of GPT-4 to achieve high pass rates in translating code to the Gemmini accelerator. We also propose a 2-phase workflow for utilizing LLMs to generate hardware-optimized code.
  arXiv  Detail & Related papers  (2024-08-06T19:10:25Z)
• Case2Code: Scalable Synthetic Data for Code Generation [105.89741089673575]
  Large Language Models (LLMs) have shown outstanding breakthroughs in code generation. Recent work improves code LLMs by training on synthetic data generated by some powerful LLMs. We propose a textbfCase2Code task by exploiting the expressiveness and correctness of programs.
  arXiv  Detail & Related papers  (2024-07-17T11:35:00Z)
• New Solutions on LLM Acceleration, Optimization, and Application [14.995654657013741]
  Large Language Models (LLMs) have become extremely potent instruments with exceptional capacities for comprehending and producing human-like text in a range of applications. However, the increasing size and complexity of LLMs present significant challenges in both training and deployment. We provide a review of recent advancements and research directions aimed at addressing these challenges.
  arXiv  Detail & Related papers  (2024-06-16T11:56:50Z)
• A Survey on Hardware Accelerators for Large Language Models [0.0]
  Large Language Models (LLMs) have emerged as powerful tools for natural language processing tasks. There is a pressing need to address the computational challenges associated with their scale and complexity.
  arXiv  Detail & Related papers  (2024-01-18T11:05:03Z)
• If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents [81.60906807941188]
  Large language models (LLMs) are trained on a combination of natural language and formal language (code) Code translates high-level goals into executable steps, featuring standard syntax, logical consistency, abstraction, and modularity.
  arXiv  Detail & Related papers  (2024-01-01T16:51:20Z)
• Simultaneous Machine Translation with Large Language Models [51.470478122113356]
  We investigate the possibility of applying Large Language Models to SimulMT tasks. We conducted experiments using the textttLlama2-7b-chat model on nine different languages from the MUST-C dataset. The results show that LLM outperforms dedicated MT models in terms of BLEU and LAAL metrics.
  arXiv  Detail & Related papers  (2023-09-13T04:06:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.