EDITH: Efficient Design for Intelligent devices exploiting emerging TecHnologies

物联网当前的趋势是在边缘设计更智能、更小、更自主的处理子系统。因此，预计未来的物联网将不会由向服务器发送数据的被动设备组成，它将更像一个分布式计算结构，其中许多物联网设备和子系统本身将处理和分析数据，以便做出自主决策。典型的目标应用包括集成在智能汽车、视频监控摄像头、移动的电话、家庭个人助理、无人机、医疗设备、工业监控等中的视频处理系统。卷积神经网络（CNN），递归神经网络（RNN）），在严格的时间和功率约束下。这类新的算法涉及矩阵的重复乘法，这是计算密集型的，需要高数据带宽。此外，电源效率要求非常高：高达10 Tera Operations Per Second（TOPS）per Watt。传统的计算体系结构并不能很好地适应这些新的要求。 在这种情况下，我们的目标是在设备的架构层面进行创新，通过考虑新兴技术，如硅光子学，这些技术在解决传统技术中的高功耗和低带宽问题方面越来越受到关注。这些技术可以集成到拟议的特定于应用的架构中，通过利用低功耗和硅光子特有的无与伦比的速度和带宽来实现CNN和RNN等深度学习算法。我们将定义一个体系结构探索流程，支持集成基于光子学的组件，分布式并行存储器和协调数据移动和计算的专用可编程处理器。我们将开发集成上述组件的新性能模型。我们还将为深度学习算法提出高效的编程和部署模型。在这个模型中，安全性将是一个cetric指标。 本文的新奇主要在于：（1）基于硅光子学新兴技术的智能器件的体系结构级和系统级视图;（2）自顶向下和自底向上方法的结合;（3）与先进的物理级研究的完美互补，提出了创新的新兴技术。

The current trend in Internet of Things is to design processing subsystems on the edge that are smarter, smaller and more autonomous. Therefore, it is expected that the Internet of Things of the future will not be composed of passive devices sending data to a server, it will be rather like a distributed computing fabric, where many of the IoT devices and subsystems themselves will process and analyze data in order to make autonomous decisions. Typical target applications include video-processing systems integrated in smart cars, video surveillance cameras, mobile phones, home personal assistants, drones, healthcare devices, industrial monitoring and control, etc. The key enablers for this new paradigm will be the innovative programmable devices and subsystems able to execute multiple complex algorithms including deep learning algorithms (ex. Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN)), under tight timing and power constraints. This new class of algorithms involve repeated multiplications of matrices, which are computationally intensive and require high data bandwidths. In addition, the power efficiency requirements are very high: up to 10 Tera Operations Per Second (TOPS) per Watt. Conventional computing architectures are not well suited to these new requirements. In this context, we aim to innovate at the architecture level of devices, by considering emerging technologies like silicon-photonics that have progressively attracted more and more attention for their use in tackling the high-power consumption and low bandwidth issues in conventional technologies. These technologies can be integrated into proposed application-specific architectures tuned to implement deep learning algorithms like CNN and RNN by exploiting the low power consumption and the unequalled speed and bandwidth specific to silicon-photonics. We will define an architecture exploration flow that supports the integration of photonics-based components, distributed parallel memories, and application-specific programmable processors that coordinate the data movement and computation. We will develop new performance models that integrate the components mentioned above. We will also propose efficient programming and deployment models for deep learning algorithms. In this models, security will be one of the cetric metrics.  The novelty of the proposed contributions resides mainly in: (1) the architecture-level and system-level view of the intelligent devices based on the silicon-photonics emerging technologies, (2) the combining of top-down and bottom up approach and (3) the perfect complementarity with the advanced physical-level research proposing innovative emergent technologies.