The increasing role of Artificial Intelligence (AI) and Machine Learning (ML) in our lives brought a paradigm shift on how and where the computation is performed. Stringent latency requirements and congested bandwidth moved AI inference from Cloud space towards end-devices. This change required a major simplification of Deep Neural Networks (DNN), with memory-wise libraries or co-processors that perform fast inference with minimal power. Unfortunately, many applications such as natural language processing, time-series analysis and audio interpretation are built on a different type of Artifical Neural Networks (ANN), the so-called Recurrent Neural Networks (RNN), which, due to their intrinsic architecture, remains too complex and heavy to run efficiently on embedded devices. 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 usage. Echo State Networks (ESN) and Liquid State Machines are the most notable examples. In this scenario, we propose a performance comparison of a ESN, designed and trained using Bayesian Optimization techniques, against current RNN solutions. We aim to demonstrate that ESN have comparable performance in terms of accuracy, require minimal training time, and they are more optimized in terms of memory usage and computational efficiency. Preliminary results show that ESN are competitive with RNN on a simple benchmark, and both training and inference time are faster, with a maximum speed-up of 2.35x and 6.60x, respectively.

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.

Nowadays, the majority of the monitoring devices used in clinical settings is limited to specific applications and powered by highly specialized microcontrollers and pre-programmed DSP systems. Moreover, these kind of devices are usually connected to a high capacity battery to operate in case of power blackout. Nevertheless, considering that all the measured bio-signals depends from an amperometric or potentiometric transducer, it should be viable to integrate them on a single device with multiple probes, reprogrammable sensor-fusion capabilities and on-board signal processing. Within this context, in this paper, we present a design concept for such a device. Exploiting FPGA reconfigurability, various analog front-ends can be connected to the device and configured to return the measured signal or the output of the desired signal processing to the user. Multiple case studies with different sensors and end-user applications are described. The high degree of parallelism and the reduced frequency of the embedded FPGA coprocessor make it suitable for all the applications that are subject to medium/low power and cost constraints such as portable Point-of-Care devices or emergency medical centers.