Tensor program tuning is essential for the efficient deployment of deep neural networks. Search-based approaches have demonstrated scalability and effectiveness in automatically finding high-performance programs for specific hardware. However, the search process is often inefficient, taking hours or even days to discover optimal programs due to the exploration mechanisms guided by an accurate but slow-learned cost model. Meanwhile, the learned cost model trained on one platform cannot seamlessly adapt online to another, which we call cross-platform online unawareness. In this work, we propose Pruner and MoA-Pruner. Pruner is a ''Draft-then-Verify'' exploration mechanism that accelerates the schedule search process. Instead of applying the complex learned cost model to all explored candidates, Pruner drafts small-scale potential candidates by introducing a naive Symbol-based Analyzer (draft model), then identifies the best candidates by the learned cost model. MoA-Pruner introduces a Momentum online Adaptation strategy to address the cross-platform online unawareness.

Extending existing architectures with customized instruction extensions is emerging to achieve high performance and energy efficiency for specific applications.Automated discovery of custom instructions (CIs) is well-studied nowadays, which requires exploring combinations of different types and quantities of operations, resulting in a vast search space.However, previous works typically use microarchitectureagnostic cost models, leading to suboptimal CIs that may degrade performance.They leverage graph isomorphism to reduce area overhead, but few of them consider its potential to benefit performance-oriented exploration.To this end, we present CIExplorer, a framework for adaptive CI exploration.

Kuzushiji is an ancient form of Japanese script that has deteriorated over time, making many characters unrecognizable. Recent font generation methods have mainly focused on modern font generation using large-scale raster images, making it difficult to apply them to ancient handwritten scripts like Kuzushiji. To address this challenge, we propose a web-based real-time application for generating Kuzushiji fonts using vector images without requiring any model training, which incorporates a differentiable rasterizer, a conditional diffusion model, a discriminator, and a Multi-Stroke Encoder for stroke-level text control. Multiple loss functions are employed to optimize font parameters. This application allows users to freely input modern Japanese characters to generate corresponding Kuzushiji images. Our results are resolution-independent and are more effective for preserving historical text. Supplementary slides: http://bit.ly/41YOyug

Deep neural networks (DNNs) have been successfully applied to a great variety of applications, ranging from small IoT devices to large scale services in a data center. In order to improve the efficiency of processing these DNN models, dedicated hardware accelerators are required for all these scenarios. Theoretically, there exists an optimized acceleration architecture for each application. However, considering the cost of chip design and corresponding tool-chain development, researchers need to trade off between efficiency and generality. In this work, we demonstrate that it is practical to use a unified architecture, called Ascend, to support those applications, ranging from IoT devices to data-center services. We provide a lot of design details to explain that the success of Ascend relies on contributions from different levels. First, heterogeneous computing units are employed to support various DNN models. And the datapath is adapted according to the requirement of computing and data access. Second, when scaling the Ascend architecture from a single core to a cluster containing thousands of cores, it involves design efforts, such as memory hierarchy and system level integration. Third, a multi-tier compiler, which provides flexible choices for developers, is the last critical piece. Experimental results show that using accelerators based on the Ascend architecture can achieve comparable or even better performance in different applications. In addition, various chips based on the Ascend architecture have been successfully commercialized. More than 100 million chips have been used in real products.