QuartDepth: Post-Training Quantization for Real-Time Depth Estimation on
  the Edge
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
67e314352fb3b4f2e999ec50
Xuan Shen
67e314352fb3b4f2e999ec51
Weize Ma
67e314352fb3b4f2e999ec52
Jing Liu
67e314352fb3b4f2e999ec53
Changdi Yang
67e314352fb3b4f2e999ec54
Rui Ding
67e314352fb3b4f2e999ec55
Quanyi Wang
67e314352fb3b4f2e999ec56
Henghui Ding
67e314352fb3b4f2e999ec57
Wei Niu
67e314352fb3b4f2e999ec58
Yanzhi Wang
67e314352fb3b4f2e999ec59
Pu Zhao
67e314352fb3b4f2e999ec5a
Jun Lin
67e314352fb3b4f2e999ec5b
Jiuxiang Gu
QuartDepth: Post-Training Quantization for Real-Time Depth Estimation on
  the Edge
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
QuartDepth improves monocular depth estimation accuracy and efficiency on ASICs through 4-bit quantization, activation polishing, and custom hardware acceleration.
monocular depth estimation ASICs post-training quantization activation quantization weight quantization activation polishing weight reconstruction kernel fusion instruction programmability energy efficiency