The increase in computational power of embedded devices and the latency demands of novel applications brought a paradigm shift on how and where the computation is performed. Although AI inference is slowly moving from the cloud to end-devices with limited resources, time-centric recurrent networks like Long-Short Term Memory remain too complex to be transferred on embedded devices without extreme simplifications and limiting the performance of many notable applications. To solve this issue, the Reservoir Computing paradigm proposes sparse, untrained non-linear networks, the Reservoir, that can embed temporal relations without some of the hindrances of Recurrent Neural Networks training, and with a lower memory occupation. Echo State Networks (ESN) and Liquid State Machines are the most notable examples. In this scenario, we propose EchoBay , a comprehensive C++ library for ESN design and training. EchoBay is architecture-agnostic to guarantee maximum performance on different devices (whether embedded or not), and it offers the possibility to optimize and tailor an ESN on a particular case study, reducing at the minimum the effort required on the user side. This can be done thanks to the Bayesian Optimization (BO) process, which efficiently and automatically searches hyper-parameters that maximize a fitness function. Additionally, we designed different optimization techniques that take in consideration resource constraints of the device to minimize memory footprint and inference time. Our results in different scenarios show an average speed-up in training time of 119x compared to Grid and Random search of hyper-parameters, a decrease of 94% of trained models size and 95% in inference time, maintaining comparable performance for the given task. The EchoBay library is Open Source and publicly available at https://github.com/necst/Echobay.

中文翻译：

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回声湾

嵌入式设备计算能力的提高和新型应用程序的延迟需求带来了计算执行方式和位置的范式转变。尽管 AI 推理正缓慢地从云端转移到资源有限的终端设备，但以时间为中心的循环网络（如长短期记忆）仍然过于复杂，无法在没有极端简化和限制许多著名应用程序的性能的情况下在嵌入式设备上传输。为了解决这个问题，Reservoir Computing 范式提出了稀疏的、未经训练的非线性网络，即 Reservoir，它可以嵌入时间关系而没有递归神经网络训练的一些障碍，并且内存占用较低。Echo State Networks (ESN) 和 Liquid State Machines 是最显着的例子。在这种情况下，我们建议回声湾，用于 ESN 设计和培训的综合 C++ 库。EchoBay 与架构无关，可确保在不同设备（无论是否嵌入式）上实现最佳性能，并且它提供了针对特定案例研究优化和定制 ESN 的可能性，从而最大限度地减少了用户方面所需的工作量。这可以通过贝叶斯优化 (BO) 过程来完成，该过程可以有效地自动搜索最大化适应度函数的超参数。此外，我们设计了不同的优化技术，考虑到设备的资源限制，以最大限度地减少内存占用和推理时间。我们在不同场景中的结果显示，与超参数的网格和随机搜索相比，训练时间的平均速度提高了 119 倍，训练模型大小减少 94%，推理时间减少 95%，保持给定任务的可比性能。EchoBay 库是开源的，可在 https://github.com/necst/Echobay 上公开获取。