HALO: Hardware-aware quantization with low critical-path-delay weights for LLM acceleration

• URL: http://arxiv.org/abs/2502.19662v2
• Date: Fri, 25 Apr 2025 09:54:59 GMT
• Title: HALO: Hardware-aware quantization with low critical-path-delay weights for LLM acceleration
• Authors: Rohan Juneja, Shivam Aggarwal, Safeen Huda, Tulika Mitra, Li-Shiuan Peh,
• Abstract summary: HALO is a versatile framework for Hardware-Aware Post-Training Quantization (PTQ)<n>Unlike traditional methods, HALO explicitly incorporates detailed hardware characteristics, including critical-path timing and power consumption.<n>Average performance improvements of 270% and energy savings of 51% over baseline quantization methods.
• Score: 5.88033624474104
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantization is critical for efficiently deploying large language models (LLMs). Yet conventional methods remain hardware-agnostic, limited to bit-width constraints, and do not account for intrinsic circuit characteristics such as the timing behaviors and energy profiles of Multiply-Accumulate (MAC) units. This disconnect from circuit-level behavior limits the ability to exploit available timing margins and energy-saving opportunities, reducing the overall efficiency of deployment on modern accelerators. To address these limitations, we propose HALO, a versatile framework for Hardware-Aware Post-Training Quantization (PTQ). Unlike traditional methods, HALO explicitly incorporates detailed hardware characteristics, including critical-path timing and power consumption, into its quantization approach. HALO strategically selects weights with low critical-path-delays enabling higher operational frequencies and dynamic frequency scaling without disrupting the architecture's dataflow. Remarkably, HALO achieves these improvements with only a few dynamic voltage and frequency scaling (DVFS) adjustments, ensuring simplicity and practicality in deployment. Additionally, by reducing switching activity within the MAC units, HALO effectively lowers energy consumption. Evaluations on accelerators such as Tensor Processing Units (TPUs) and Graphics Processing Units (GPUs) demonstrate that HALO significantly enhances inference efficiency, achieving average performance improvements of 270% and energy savings of 51% over baseline quantization methods, all with minimal impact on accuracy.

Related papers

• R-Sparse: Rank-Aware Activation Sparsity for Efficient LLM Inference [77.47238561728459]
  R-Sparse is a training-free activation sparsity approach capable of achieving high sparsity levels in advanced LLMs. Experiments on Llama-2/3 and Mistral models across ten diverse tasks demonstrate that R-Sparse achieves comparable performance at 50% model-level sparsity.
  arXiv  Detail & Related papers  (2025-04-28T03:30:32Z)
• LLMPi: Optimizing LLMs for High-Throughput on Raspberry Pi [0.48212500317840945]
  Large Language Models (LLMs) on resource-constrained edge devices like the Raspberry Pi presents challenges in computational efficiency, power consumption, and response latency. This paper explores quantization-based optimization techniques to enable high- throughput, energy-efficient execution of LLMs on low-power embedded systems. Our findings highlight the potential of quantized LLMs for real-time conversational AI on edge devices, paving the way for low-power, high-efficiency AI deployment in mobile and embedded applications.
  arXiv  Detail & Related papers  (2025-04-02T20:29:39Z)
• QuartDepth: Post-Training Quantization for Real-Time Depth Estimation on the Edge [55.75103034526652]
  We propose QuartDepth which adopts post-training quantization to quantize MDE models with hardware accelerations for ASICs. Our approach involves quantizing both weights and activations to 4-bit precision, reducing the model size and computation cost. We design a flexible and programmable hardware accelerator by supporting kernel fusion and customized instruction programmability.
  arXiv  Detail & Related papers  (2025-03-20T21:03:10Z)
• FBQuant: FeedBack Quantization for Large Language Models [13.545647487024864]
  We propose FeedBack Quantization (FBQuant), a novel approach inspired by negative feedback mechanisms in automatic control. FBQuant inherently ensures that the reconstructed weights remain bounded by quantization, thereby reducing the risk of overfitting. For 3-bit Llama2-7B, FBQuant improves zero-shot accuracy by 1.2%.
  arXiv  Detail & Related papers  (2025-01-25T06:04:07Z)
• USEFUSE: Utile Stride for Enhanced Performance in Fused Layer Architecture of Deep Neural Networks [0.6435156676256051]
  This study presents the Sum-of-Products (SOP) units for convolution, which utilize low-latency left-to-right bit-serial arithmetic. An effective mechanism detects and skips inefficient convolutions after ReLU layers, minimizing power consumption. Two designs cater to varied demands: one focuses on minimal response time for mission-critical applications, and another focuses on resource-constrained devices with comparable latency.
  arXiv  Detail & Related papers  (2024-12-18T11:04:58Z)
• MixPE: Quantization and Hardware Co-design for Efficient LLM Inference [16.42907854119748]
  MixPE is a specialized mixed-precision processing element designed for efficient low-bit quantization in large language models. We show that MixPE surpasses the state-of-the-art quantization accelerators by $2.6times$ speedup and $1.4times$ energy reduction.
  arXiv  Detail & Related papers  (2024-11-25T07:34:53Z)
• MobileQuant: Mobile-friendly Quantization for On-device Language Models [31.75012542498791]
  Large language models (LLMs) have revolutionized language processing, delivering outstanding results across multiple applications. deploying LLMs on edge devices poses several challenges with respect to memory, energy, and compute costs. We introduce a simple post-training quantization method, named MobileQuant, that extends previous weight equivalent transformation works.
  arXiv  Detail & Related papers  (2024-08-25T20:41:22Z)
• BoA: Attention-aware Post-training Quantization without Backpropagation [11.096116957844014]
  Post-training quantization (PTQ) is a promising solution for deploying large language models (LLMs) on resource-constrained devices. We introduce a novel backpropagation-free PTQ algorithm that optimize integer weights by considering inter-layer dependencies.
  arXiv  Detail & Related papers  (2024-06-19T11:53:21Z)
• SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models [67.67135738642547]
  Post-training quantization (PTQ) is a powerful compression technique investigated in large language models (LLMs) Existing PTQ methods are not ideal in terms of accuracy and efficiency, especially with below 4 bit-widths. This paper presents a Salience-Driven Mixed-Precision Quantization scheme for LLMs, namely SliM-LLM.
  arXiv  Detail & Related papers  (2024-05-23T16:21:48Z)
• Gradient-based Automatic Mixed Precision Quantization for Neural Networks On-Chip [0.9187138676564589]
  We present High Granularity Quantization (HGQ), an innovative quantization-aware training method. HGQ fine-tune the per-weight and per-activation precision by making them optimizable through gradient descent. This approach enables ultra-low latency and low power neural networks on hardware capable of performing arithmetic operations.
  arXiv  Detail & Related papers  (2024-05-01T17:18:46Z)
• EdgeQAT: Entropy and Distribution Guided Quantization-Aware Training for the Acceleration of Lightweight LLMs on the Edge [40.85258685379659]
  Post-Training Quantization (PTQ) methods degrade in quality when quantizing weights, activations, and KV cache together to below 8 bits. Many Quantization-Aware Training (QAT) works quantize model weights, leaving the activations untouched, which do not fully exploit the potential of quantization for inference acceleration on the edge. We propose EdgeQAT, the Entropy and Distribution Guided QAT for the optimization of lightweight LLMs to achieve inference acceleration on Edge devices.
  arXiv  Detail & Related papers  (2024-02-16T16:10:38Z)
• QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models [85.02796681773447]
  We propose a quantization-aware low-rank adaptation (QA-LoRA) algorithm. The motivation lies in the imbalanced degrees of freedom of quantization and adaptation. QA-LoRA is easily implemented with a few lines of code.
  arXiv  Detail & Related papers  (2023-09-26T07:22:23Z)
• On-Chip Hardware-Aware Quantization for Mixed Precision Neural Networks [52.97107229149988]
  We propose an On-Chip Hardware-Aware Quantization framework, performing hardware-aware mixed-precision quantization on deployed edge devices. For efficiency metrics, we built an On-Chip Quantization Aware pipeline, which allows the quantization process to perceive the actual hardware efficiency of the quantization operator. For accuracy metrics, we propose Mask-Guided Quantization Estimation technology to effectively estimate the accuracy impact of operators in the on-chip scenario.
  arXiv  Detail & Related papers  (2023-09-05T04:39:34Z)
• AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration [54.692405042065815]
  We propose Activation-aware Weight Quantization (AWQ), a hardware-friendly approach for LLM low-bit weight-only quantization. AWQ protects only 1% salient weights and achieves excellent quantization performance for instruction-tuned LMs and, for the first time, multi-modal LMs. We also implement TinyChat, an efficient and flexible inference framework tailored for 4-bit on-device LLM/VLMs.
  arXiv  Detail & Related papers  (2023-06-01T17:59:10Z)
• Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks [103.22651843174471]
  This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which aggregates them into a quantized global model and synchronizes the devices. We show that the FL training process can be described as a Markov decision process and propose a model-based reinforcement learning (RL) method to optimize action selection over iterations.
  arXiv  Detail & Related papers  (2022-09-21T08:52:51Z)
• Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference [56.24109486973292]
  We study the interplay between pruning and quantization during the training of neural networks for ultra low latency applications. We find that quantization-aware pruning yields more computationally efficient models than either pruning or quantization alone for our task.
  arXiv  Detail & Related papers  (2021-02-22T19:00:05Z)
• EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference [82.1584439276834]
  Transformer-based language models such as BERT provide significant accuracy improvement for a multitude of natural language processing (NLP) tasks. We present EdgeBERT, an in-depth algorithm- hardware co-design for latency-aware energy optimization for multi-task NLP.
  arXiv  Detail & Related papers  (2020-11-28T19:21:47Z)

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.