Abstract
This paper describes the design and implementation of a coarse-grained reconfigurable array (CGRA) for low-power biological signal processing. It uses an use-case-driven approach which explores the application domain and gathers common requirements. The selected CGRA core architecture is implemented using a standard-cell flow (in a generic 90nm CMOS process), so that the CGRA can be totally or partially turned off by power gating. The selected CGRA design is evaluated for two use-cases using layout information and accurate node activity information. The resulting accelerator is capable of performing various signal processing tasks very efficiently, achieving an average power consumption of 19.9 pJ/cycle (or 1.99mW at 100 MHz). Static power consumption for less intensive tasks can be reduced by using only some sections of the CGRA while powering-off others.

Abstract

Abstract
Filter-bank Multicarrier (FBMC) modulation has been proposed as a 5G waveform candidate due to its better spectral efficiency and lower out-of-band emissions compared to OFDM. This paper presents an FPGA-based implementation of a Frequency Spreading FBMC-OQAM baseband modulator and evaluates it in terms of performance, resource utilization and power consumption. The proposed system is then compared with published Polyphase Network (PPN) FBMC-OQAM designs, focusing on resource utilization. The results suggest that the higher computational complexity of FS-FBMC systems does not directly result in higher resource utilization, which makes FS-FBMC a convenient scheme for implementing FBMC designs on FPGA.

Abstract
The usage of application-specific hardware based on Field-Programmable Gate Arrays (FPGA) has proven its benefits. Current system-on-chips, which contain FPGA fabric, supporting dynamic partial reconfiguration, enable a dynamic hardware acceleration for hardware/software co-designs. With the trend to consolidate multiple computing systems into a single system, applications with mixed criticalities can come into conflict. With our approach, we are exploring the possibility to utilize dedicated hardware for the system management and benefit from possible parallelization of applications and system management tasks.

Abstract

Abstract
The incidence of chronic diseases is increasing and monitoring patients in a home environment is recommended. Noncompliance with prescribed medication regimens is a concern, especially among older people. Heart failure is a chronic disease that requires patients to follow strict medication plans permanently. With the objective of helping these patients managing information about their medicines and increasing adherence, the personal medication advisor CARMIE was developed as a conversational agent capable of interacting, in Portuguese, with users through spoken natural language. The system architecture is based on a language parser, a dialog manager, and a language generator, integrated with already existing tools for speech recognition and synthesis. All modules work together and interact with the user through an Android application, supporting users to manage information about their prescribed medicines. The authors also present a preliminary usability study and further considerations on CARMIE.