Diffusion models have achieved remarkable success in the image and video generation tasks. Nevertheless, they often require a large amount of memory and time overhead during inference, due to the complex network architecture and considerable number of timesteps for iterative diffusion. Recently, the post-training quantization (PTQ) technique has proved a promising way to reduce the inference cost by quantizing the float-point operations to low-bit ones. However, most of them fail to tackle with the large variations in the distribution of activations across distinct channels and timesteps, as well as the inconsistent of input between quantization and inference on diffusion models, thus leaving much room for improvement. To address the above issues, we propose a novel method dubbed Timestep-Channel Adaptive Quantization for Diffusion Models (TCAQ-DM). Specifically, we develop a timestep-channel joint reparameterization (TCR) module to balance the activation range along both the timesteps and channels, facilitating the successive reconstruction procedure. Subsequently, we employ a dynamically adaptive quantization (DAQ) module that mitigate the quantization error by selecting an optimal quantizer for each post-Softmax layers according to their specific types of distributions. Moreover, we present a progressively aligned reconstruction (PAR) strategy to mitigate the bias caused by the input mismatch. Extensive experiments on various benchmarks and distinct diffusion models demonstrate that the proposed method substantially outperforms the state-of-the-art approaches in most cases, especially yielding comparable FID metrics to the full precision model on CIFAR-10 in the W6A6 setting, while enabling generating available images in the W4A4 settings.

Point clouds play a crucial role in various fields such as robotics, 3D modeling, and autonomous driving. DGCNN, as a representative work in this domain, has exhibited superior performance compared to classic models like PointNet++. Such models often display significant computational demands and encounter notable performance bottlenecks, so how to accelerate their calculations has become a noteworthy issue. Nevertheless, due to the inherent disparities between point cloud tasks and conventional convolution tasks, existing algorithms and architectural paradigms designed for convolution frequently prove to be ill-suited for point clouds, such as Tensor Core (TC). There have been studies focused on reducing DGCNN model complexity to improve computational efficiency, but research specifically tailored to TC acceleration remains relatively limited. Therefore, we present an encapsulation module that bridges the computation of model in Torch level with the components of the CUDA backend, introducing TC into DGCNN. It dynamically adjusts parameters and computation flow based on input data and transforms batch computations into streaming computations. Additionally, we propose a new computational organization approach, which involves the restructuring of two critical computation steps within DGCNN: the Get Graph Feature operation and the Conv operation. It significantly enhances the computational speed of these two operations while reducing memory redundancy of tensor data involved in the intermediate processes. In the experiment, our modifications achieve an average acceleration of 1.80X for the Get Graph Feature operation and 1.46X for the Conv operation, all while not compromising result accuracy.

For mobile apps, user-perceived delays are critical for user satisfaction. According to our measurement, long delays are commonly caused by network and storage I/O operations while short delays are mainly caused by UI rendering. Short delays are not uncommon, which account for 55.3% in our measurement cases. Previous app performance studies have largely focused on I/O operations but the understanding of UI rendering impact is limited. In this work, we propose DRAW, a system that performs two UI rendering analyses to help app developers pinpoint rendering problems and resolve short delays. The first analysis outlines the wasted rendering time on invisible or covered UI components, namely the overdraw problem. The second analysis is to identify the responsible UI components and rendering operations that cause overall low rendering efficiency. We implement DRAW on Android and apply it to study 1,158 real-world Android apps. Results show that DRAW is helpful as it can pinpoint the responsible UI components and specific rendering operations. Four concrete case studies of real-world apps are further presented to show how DRAW can help developers improve the UI rendering performance of their apps.

The past decade has witnessed a tremendous growth in the variety and complexity of mobile applications (apps). Although considerable amount of efforts have been spent to improve app performance, smartphones nowadays still face many performance challenges. We discover that the resource contention of multiple running apps, caused by resource bottleneck(s), is a key factor that affects the smartphone performance. In this paper, we present APB, an Automatic tool that detects Performance issues caused by resource Bottleneck(s) on commodity Android smartphones. APB employs an innovative bottleneck-hypersurface model to quantify performance issues given a specific system state. Then, based on the model, APB identifies a list of apps that contribute most to the resource contention, which can well inform the end user to take action such as killing background apps to resolve the performance issue. We implement APB on commodity Android platforms and widely evaluate its effectiveness with real user studies. Results show that APB outperforms three baseline approaches and helps users to improve smartphone performance by 10 to 67 percent, with less than 1 percent runtime overhead.