MediaTek
NeuroPilot 人工智能
引领终端人工智能技术创新
Edge AI
The rapid evolution of AI-enhanced use cases is changing how devices are being created and used. This shift in demand, as well as the increasing capability of AI technologies, is motivating products to process AI-enhancements ‘at the edge’ in products at home, in vehicle or about person, rather than rely solely on Cloud-connected support.
Advantages of this edge-computing include real-time immediacy, data privacy and overall lower power consumption. For device makers, there’s also no need to roll out supporting Cloud infrastructure into every region a product is sold into, decreasing time to market.
MediaTek Neural Processing Unit (NPU)
MediaTek develops its own Deep Learning Accelerators (Performance Cores), Visual Processing Units (Flexible Cores), hardware-based, multicore scheduler, and software development kits (NeuroPilot) that make up the core components of its industry-leading Neural Processing Units (NPUs).
MediaTek NeuroPilot
We’re meeting the Edge AI challenge head-on with MediaTek NeuroPilot. Through the heterogeneous computing capabilities in our SoC's such as APUs, GPUs and CPUs, we are providing high-performance and power efficiency for AI features and applications. Developers can target these specific processing units within the chip, or, they can let MediaTek NeuroPilot SDK intelligently handle the processing allocation for them.
联发科生成式AI
作为开发功能强大， 高度集成和出色能效 SoC 产品的行业先行者，联发科技通过创建终端人工智能处理平台生态系统，在其产品系列包括从智能手机到智能家居、可穿戴、物联网及联网汽车中搭配软件工具，以实现人工智能的未来。
人工智能时代的到来
现在
随着人工智能的快速发展，它正在重塑我们在家里，工作场所和城市中所使用的技术，并为我们带来新的体验以及改变我们的互动方式。
现在，人工智能能实现如深度学习面部检测（DL-FD），实时美化，创意图层堆栈，物体和场景辨识，AR/MR 加速， 摄影和视频的增强现实等技术。
明天及未来
先进人工智能设备的未来是巨大的。想象一下，未来可使用符合您需求和习惯的设备：智能手机可以追踪您的健康状况，并在您感到不舒服之前先提醒您注意; 智能家居的环境，可以在您到家之前就先开启家中的电灯和暖气; 或乘坐自动驾驶汽车，可以载您到任何您想去的地方。这种与生俱来的智能如此贴近我们的生活，带来了新的用户体验水平，并改变了您的世界。这就是终端人工智能落地的体现。
联发科技 NeuroPilot 特点
一次编写, 随处应用
联发科技 NeuroPilot SDK 支持所有联发科技具人工智能的硬件。 它允许开发人员为现有和未来的联发科技硬件平台以及包括 智能手机，汽车，智能家居，物联网等在内的所有产品线做“一次编写，随处应用”。 这不仅简化创建过程，也节省成本和上市时间。 其所支持的软件生态系统包括安卓和 Linux 操作系统， 并提供完整的编译程序，分析器和应用程序库。
构建友善的架构
开发者可以使用 TensorFlow，TF Lite，Caffe，Caffe2 Amazon MXNet，Sony NNabla 或其他自定义的第三方通用架构来构建应用程序。在 API 级别，我们提供联发科技 NeuroPilot SDK 包括谷歌安卓神经网络 API（ Android NNAPI ）和联发科技 NeuroPilot 扩充组件，从而让开发人员和设备制造商能以更加贴近硬件的方式编码以提高性能和功效。
联发创新基地
联发创新基地致力于发展和提升日常设备中的 AI 生态系统。研究探索机器学习（ML）的先进技术，开发创新产品，确保 MediaTek 硬件和软件解决方案在各自的品牌垂直领域继续引领行业。该国际研究团队定期在全球享有盛名的出版物上发表研究论文。
Mar 8, 2023
Extending the Pre-Training of BLOOM for Improved Support of Traditional Chinese: Models, Methods and Results
In this paper we present the multilingual language model BLOOM-zh that features enhanced support for Traditional Chinese. BLOOM-zh has its origins in the open-source BLOOM models presented by BigScience in 2022.
Feb 02, 2023
Fisher-Legendre (FishLeg) optimization of deep neural networks
We introduce a new approach to estimate the natural gradient via Legendre-Fenchel duality, provide a convergence proof, and show competitive performance on a number of benchmarks.
Dec 19, 2022
A Learning-Based Algorithm for Early Floorplan With Flexible Blocks
This paper presents a learning-based algorithm using graph neural network (GNN) and deconvolution network to predict the placement of the locations and the aspect ratios for the design blocks with flexible rectangles.
Oct 31, 2022
Near-Optimal Collaborative Learning in Bandits
A near-optimal algorithm is proposed for pure exploration in a new framework for collaborative bandit learning that encompasses recent prior works.
Nov 24, 2022
Gradient Descent: Robustness to Adversarial Corruption
We provide performance guarantees for gradient descent under a general adversarial framework
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning
Kernel-based models such as kernel ridge regression and Gaussian processes are ubiquitous in machine learning applications for regression and optimization.
Feb 20, 2022
Regret Bounds for Noise-Free Kernel-Based Bandits
Kernel-based bandit is an extensively studied black-box optimization problem, in which the objective function is assumed to live in a known reproducing kernel Hilbert space.
LPI: Learned Positional Invariances for Transfer of Task Structure and Zero-shot Planning
Real-world tasks often include interactions with the environment where our actions can drastically change the available or desirable long-term outcomes.
Jun 1, 2022
Adaptive erasure of spurious sequences in sensory cortical circuits
Sequential activity reflecting previously experienced temporal sequences is considered a hallmark of learning across cortical areas.
Apr 13, 2022
Flexible Multiple-Objective Reinforcement Learning for Chip Placement
Recently, successful applications of reinforcement learning to chip placement have emerged. Pretrained models are necessary to improve efficiency and effectiveness.
