Fast Hybrid Cascade for Voxel-based 3D Object Classification

Voxel-based 3D object classification has been frequently studied in recent years. The previous methods often directly convert the classic 2D convolution into a 3D form applied to an object with binary voxel representation. In this paper, we investigate the reason why binary voxel representation is not very suitable for 3D convolution and how to simultaneously improve the performance both in accuracy and speed. We show that by giving each voxel a signed distance value, the accuracy will gain about 30% promotion compared with binary voxel representation using a two-layer fully connected network. We then propose a fast fully connected and convolution hybrid cascade network for voxel-based 3D object classification. This threestage cascade network can divide 3D models into three categories: easy, moderate and hard. Consequently, the mean inference time (0.3ms) can speedup about 5x and 2x compared with the state-of-the-art point cloud and voxel based methods respectively, while achieving the highest accuracy in the latter category of methods (92%). Experiments with ModelNet andMNIST verify the performance of the proposed hybrid cascade network

HAPI: Hardware-Aware Progressive Inference

Convolutional neural networks (CNNs) have recently become the state-of-the-art in a diversity of AI tasks. Despite their popularity, CNN inference still comes at a high computational cost. A growing body of work aims to alleviate this by exploiting the difference in the classification difficulty among samples and early-exiting at different stages of the network. Nevertheless, existing studies on early exiting have primarily focused on the training scheme, without considering the use-case requirements or the deployment platform. This work presents HAPI, a novel methodology for generating high-performance early-exit networks by co-optimising the placement of intermediate exits together with the early-exit strategy at inference time. Furthermore, we propose an efficient design space exploration algorithm which enables the faster traversal of a large number of alternative architectures and generates the highest-performing design, tailored to the use-case requirements and target hardware. Quantitative evaluation shows that our system consistently outperforms alternative search mechanisms and state-of-the-art early-exit schemes across various latency budgets. Moreover, it pushes further the performance of highly optimised hand-crafted early-exit CNNs, delivering up to 5.11x speedup over lightweight models on imposed latency-driven SLAs for embedded devices.Comment: Accepted at the 39th International Conference on Computer-Aided Design (ICCAD), 202

Rascal: From Algebraic Specification to Meta-Programming

Algebraic specification has a long tradition in bridging the gap between specification and programming by making specifications executable. Building on extensive experience in designing, implementing and using specification formalisms that are based on algebraic specification and term rewriting (namely Asf and Asf+Sdf), we are now focusing on using the best concepts from algebraic specification and integrating these into a new programming language: Rascal. This language is easy to learn by non-experts but is also scalable to very large meta-programming applications. We explain the algebraic roots of Rascal and its main application areas: software analysis, software transformation, and design and implementation of domain-specific languages. Some example applications in the domain of Model-Driven Engineering (MDE) are described to illustrate this.Comment: In Proceedings AMMSE 2011, arXiv:1106.596