A prototype of a radar system for classification of multiple targets passing through a road gate is presented in this paper. It allows to identify different types of targets, i.e., pedestrians, motorcycles, cars, and trucks. The developed system is based on a low-cost 24 GHz off-the-shelf FMCW radar, combined with an embedded Raspberry Pi PC for data acquisition and transmission to a remote processing PC, which take care of detection and classification. The processing chain relies upon a tracking algorithm to follow the targets during traversal, combined with a classification scheme based on support vector machines. The approach has been validated with experimental data acquired in different scenarios, showing good identification capabilities.

Embedding Machine Learning enables integrating intelligence in recent application domains such as Internet of Things, portable healthcare systems, and wearable devices. This paper presents an assessment of approximate computing methods at algorithmic, architecture, and circuit levels and draws perspectives for further developments and applications. The main goal is to investigate how approximate computing may reduce the complexity and enable the feasibility of embedded Machine Learning (ML) systems. Though ML is a powerful paradigm for applications in the perceptual domain (i.e. vision, touch, hearing, etc.), their computational complexity is very high and consequently real time operation and ultra-low power are still very challenging objectives. On the other hand, approximate computing has emerged as an effective solution to reduce hardware complexity, time latency and to increase energy efficiency.

Approximate computing circuits are considered as a promising solution to reduce the power consumption in embedded data processing. This paper proposes an FPGA implementation for an approximate multiplier based on inexact adder circuits. The performance of the proposed multiplier is evaluated by comparing the power consumption, the accuracy of computation, and the time delay with those of an approximate multiplier based on exact adder presented in literature. Results reports a power saving up to 17.39% with an improvement in time delay by 13.49%, at cost of less than 5% of accuracy loss.

Enabling touch-sensing capability would help appliances understand interaction behaviors with their surroundings. Many recent studies are focusing on the development of electronic skin because of its necessity in various application domains, namely autonomous artificial intelligence (e.g., robots), biomedical instrumentation, and replacement prosthetic devices. An essential task of the electronic skin system is to locally process the tactile data and send structured information either to mimic human skin or to respond to the application demands. The electronic skin must be fabricated together with an embedded electronic system which has the role of acquiring the tactile data, processing, and extracting structured information. On the other hand, processing tactile data requires efficient methods to extract meaningful information from raw sensor data. Machine learning represents an effective method for data analysis in many domains: it has recently demonstrated its effectiveness in processing tactile sensor data. In this framework, this paper presents the implementation of digital signal processing based on FPGAs for tactile data processing. It provides the implementation of a tensorial kernel function for a machine learning approach. Implementation results are assessed by highlighting the FPGA resource utilization and power consumption. Results demonstrate the feasibility of the proposed implementation when real-time classification of input touch modalities are targeted.

Developing portable autonomous systems is highly requested for numerous application domains such as Internet of Things (IoT), wearable devices, and biomedical applications. Portable systems usually contain autonomous and networked sensors; each sensor hosts multiple input channels (e.g. tactile) closely coupled to embedded computing unit and power supply. The embedded computing unit should locally extract meaningful information by employing sophisticated methods. This imposes challenges on real time operation and adds a burden regarding power consumption. Approximate or inexact computing represents a promising solution for energy efficient data processing; it tunes the accuracy of computation on the specific application requirements in order to reduce power consumption. In this paper, inexact arithmetic circuits have been employed to improve the energy efficiency for sensors digital data processing. The proposed inexact circuits achieve up to 80% power saving when compared to the exact one, and similar solutions presented in literature with a maximum loss of 1.39 dB in output SNR when applied to FIR filters.

Nowadays many application domains require an embedded electronic system for tactile data processing. Our research aims to implement a real time embedded electronic system based on tensorial kernel approach for tactile data processing. Singular value decomposition represents the more computational expensive algorithm for the tensorial kernel approach. This paper presents an assessment of FPGA implementations of one sided Jacobi algorithm for singular value decomposition. Designs are implemented to handle an arbitrary m×n matrix with fixed point arithmetic. The results figure out an efficient implementation suitable for real time embedded applications.