Towards lowpower random forest using asynchronous computing with streams We propose a sensor architecture for the internet of things (IoT), smartdust or edgeintelligence (EI) that combines nearanalogmemory (NAM) processing and asynchronous computing with streams (ACS) addressing the need for machine learning (ML) capabilities at low power budgets. In ACS an analog value is mapped to an asynchronous stream that can take one of two values (v h , v l ). This streambased data representation enables areapower efficient computing units such as the multiplier implemented as an AND gate yielding savings in power of 90% compared with digital approaches. However, a major bottleneck for computing on streams, vision sensors, and NAM approaches is the cost of analogtodigital (ADC) and digitaltostreamtodigital converters. Our NAMACS architecture, simplifies the sensor and eliminates the need for the expensive conversions. The architecture is tailored for random forest (Raf), a ML algorithm, chosen for its ability to classify using a reduced number of features. Our simulations show that using an analogmemory array of 256 512, the power consumption of the ACScore combined with the memory interface is comparable with the consumption of an ADC based memory interface, obtaining an accuracy of 83%.

Artificial Vision on Edge IoT Devices: A Practical Case for 3D Data Classification Nowadays, with the huge advance of sensor technology and the increase of the amount of data generated by them, techniques have to be developed to be able to process all this amount of information in realtime applications on edge devices, close to where data is being generated. If all that information has to be sent to the cloud to be processed, it has certain disadvantages in terms of latency, bandwidth, privacy and reliability, compared to locally processing it on the edge. In this paper, the implementation of deep learning algorithms in low power and limited resources devices in an Internet of Things scenario is studied. In order to work in realtime applications, the influence of different low power consumption deep learning hardware accelerators is studied. Finally, a practical case for smart farming is shown with comparative results in terms of power consumption and performance when running the same artificial vision algorithm on different devices.