title: QuartDepth: Post-Training Quantization for Real-Time Depth Estimation on
    the Edge
summary: Monocular Depth Estimation (MDE) has emerged as a pivotal task in computer
  vision, supporting numerous real-world applications. However, deploying
  accurate depth estimation models on resource-limited edge devices, especially
  Application-Specific Integrated Circuits (ASICs), is challenging due to the
  high computational and memory demands. Recent advancements in foundational
  depth estimation deliver impressive results but further amplify the difficulty
  of deployment on ASICs. To address this, 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. To mitigate the
  performance degradation, we introduce activation polishing and compensation
  algorithm applied before and after activation quantization, as well as a weight
  reconstruction method for minimizing errors in weight quantization.
  Furthermore, we design a flexible and programmable hardware accelerator by
  supporting kernel fusion and customized instruction programmability, enhancing
  throughput and efficiency. Experimental results demonstrate that our framework
  achieves competitive accuracy while enabling fast inference and higher energy
  efficiency on ASICs, bridging the gap between high-performance depth estimation
  and practical edge-device applicability. Code:
  https://github.com/shawnricecake/quart-depth
paper_authors._id: 67e314352fb3b4f2e999ec50
paper_authors.name: Xuan Shen
paper_authors._id: 67e314352fb3b4f2e999ec51
paper_authors.name: Weize Ma
paper_authors._id: 67e314352fb3b4f2e999ec52
paper_authors.name: Jing Liu
paper_authors._id: 67e314352fb3b4f2e999ec53
paper_authors.name: Changdi Yang
paper_authors._id: 67e314352fb3b4f2e999ec54
paper_authors.name: Rui Ding
paper_authors._id: 67e314352fb3b4f2e999ec55
paper_authors.name: Quanyi Wang
paper_authors._id: 67e314352fb3b4f2e999ec56
paper_authors.name: Henghui Ding
paper_authors._id: 67e314352fb3b4f2e999ec57
paper_authors.name: Wei Niu
paper_authors._id: 67e314352fb3b4f2e999ec58
paper_authors.name: Yanzhi Wang
paper_authors._id: 67e314352fb3b4f2e999ec59
paper_authors.name: Pu Zhao
paper_authors._id: 67e314352fb3b4f2e999ec5a
paper_authors.name: Jun Lin
paper_authors._id: 67e314352fb3b4f2e999ec5b
paper_authors.name: Jiuxiang Gu
paper_title: QuartDepth: Post-Training Quantization for Real-Time Depth Estimation on
    the Edge
paper_summary: Monocular Depth Estimation (MDE) has emerged as a pivotal task in computer
  vision, supporting numerous real-world applications. However, deploying
  accurate depth estimation models on resource-limited edge devices, especially
  Application-Specific Integrated Circuits (ASICs), is challenging due to the
  high computational and memory demands. Recent advancements in foundational
  depth estimation deliver impressive results but further amplify the difficulty
  of deployment on ASICs. To address this, 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. To mitigate the
  performance degradation, we introduce activation polishing and compensation
  algorithm applied before and after activation quantization, as well as a weight
  reconstruction method for minimizing errors in weight quantization.
  Furthermore, we design a flexible and programmable hardware accelerator by
  supporting kernel fusion and customized instruction programmability, enhancing
  throughput and efficiency. Experimental results demonstrate that our framework
  achieves competitive accuracy while enabling fast inference and higher energy
  efficiency on ASICs, bridging the gap between high-performance depth estimation
  and practical edge-device applicability. Code:
  https://github.com/shawnricecake/quart-depth
paper_ai_summary: QuartDepth improves monocular depth estimation accuracy and efficiency on ASICs through 4-bit quantization, activation polishing, and custom hardware acceleration.
paper_ai_keywords: monocular depth estimation ASICs post-training quantization activation quantization weight quantization activation polishing weight reconstruction kernel fusion instruction programmability energy efficiency