Research

global library cooperative

Research libraries are establishing BRI services to support researchers & administrators alike. Learn about BRI efforts at three US research universities.

Ideas, Insight & Inspiration

In this webinar, librarians from three research universities describe the services, collaborations, and strategies they are employing to develop research impact services at their institutions.

DuckDuckGo. Privacy, Simplified.

This page documents the utility functions and data structures that support the FFI layer by providing JSON parsing, JSON construction, error handling, and helper operations. These utilities bridge the

:koko: Compiler-enforced homogeneous JSON objects for Purescript - Risto-Stevcev/purescript-homogeneous-objects

Tuple data type and utility functions

Kernels & AI inference engine for mobile devices

AbstractAs the need for on-device artificial intelligence (AI) has increased in recent years, mobile devices tend to be equipped with multiple heterogeneous processors, including CPU, GPU, and Neural Processing Unit (NPU). While NPUs can offer low-cost ...

Real-time object detection demands high throughput and low latency, necessitating the use of hardware accelerators. NPU is specialized hardware designed to accelerate the calculation of deep learning models, providing better energy efficiency and parallel processing performance than existing CPUs or GPUs. In particular, it plays an important role in reducing latency and improving processing speed in applications that require real-time processing. In this paper, we construct a real-time object detection system based on YOLOv3, utilizing Neubla’s Antara NPU, and propose two approaches for performance optimization. First, we ensure the continuity of NPU inference by allowing the CPU to process data in advance through double buffering. Second, in a multi-NPU environment, we distribute tasks among NPUs through queue-based processing and analyze the performance limits using Amdahl’s law. Experimental results demonstrate that compared to a CPU-only environment, applying the NPU in single buffering improved throughput by 2.13 times, double buffering by 3.35 times, and in a multi-NPU environment by 4.81 times. Latency decreased by 1.6 times in single and double buffering, and by 1.18 times in the multi-NPU environment. The accuracy remained consistent, with 31.4 mAP on the CPU and 31.8 mAP on the NPU.

As the need for on-device artificial intelligence (AI) has increased in recent years, mobile devices tend to be equipped with multiple heterogeneous processors, including CPU, GPU, and Neural Processing Unit (NPU). While NPUs can offer low-cost and real-time AI processing capabilities for Deep Neural Network (DNN) inference, its limited resources often lead to a trade-off between performance and accuracy, potentially resulting in a non-trivial accuracy drop. To address this problem, we propose a new NPU–GPU Scheduling (NGS) framework for DNN-based video analytics. The main challenge lies in determining when and how to execute inference on the NPU/GPU to satisfy the performance objectives. To make more precise scheduling decisions, we first propose a new image complexity assessment model to replace the existing normalized edge density metric. Then, we formulate the scheduling problem with the objective of maximizing inference accuracy under the given latency constraint, and introduce an adaptive solution based on dynamic programming to determine which frames should be processed on the GPU and when to exit from inference for each of them. Extensive experiments conducted on a real mobile device show that our NGS framework substantially outperforms other solutions, and achieves a close-to-oracle performance.

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Over the past two decades, we have seen a rapid technological evolution of object detection and its profound impact on the entire computer vision field. If we consider today’s object detection technique as a revolution driven by deep learning, then, back in the 1990s, we would see the ingenious thinking and long-term perspective design of early computer vision. This article extensively reviews this fast-moving research field in the light of technical evolution, spanning over a quarter-century’s time (from the 1990s to 2022). A number of topics have been covered in this article, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speedup techniques, and recent state-of-the-art detection methods.