Vega: A Ten-Core SoC for IoT Endnodes with DNN Acceleration and Cognitive Wake-Up from MRAM-Based State-Retentive Sleep Mode

The Internet-of-Things (IoT) requires endnodes with ultra-low-power always-on capability for a long battery lifetime, as well as high performance, energy efficiency, and extreme flexibility to deal with complex and fast-evolving near-sensor analytics algorithms (NSAAs). We present Vega, an IoT endnode system on chip (SoC) capable of scaling from a 1.7- μW fully retentive cognitive sleep mode up to 32.2-GOPS (at 49.4 mW) peak performance on NSAAs, including mobile deep neural network (DNN) inference, exploiting 1.6 MB of state-retentive SRAM, and 4 MB of non-volatile magnetoresistive random access memory (MRAM). To meet the performance and flexibility requirements of NSAAs, the SoC features ten RISC-V cores: one core for SoC and IO management and a nine-core cluster supporting multi-precision single instruction multiple data (SIMD) integer and floating-point (FP) computation. Vega achieves the state-of-the-art (SoA)-leading efficiency of 615 GOPS/W on 8-bit INT computation (boosted to 1.3 TOPS/W for 8-bit DNN inference with hardware acceleration). On FP computation, it achieves the SoA-leading efficiency of 79 and 129 GFLOPS/W on 32- and 16-bit FP, respectively. Two programmable machine learning (ML) accelerators boost energy efficiency in cognitive sleep and active states

BioWolf: A Sub-10-mW 8-Channel Advanced Brain-Computer Interface Platform with a Nine-Core Processor and BLE Connectivity

Advancements in digital signal processing (DSP) and machine learning techniques have boosted the popularity of brain-computer interfaces (BCIs), where electroencephalography is a widely accepted method to enable intuitive human-machine interaction. Nevertheless, the evolution of such interfaces is currently hampered by the unavailability of embedded platforms capable of delivering the required computational power at high energy efficiency and allowing for a small and unobtrusive form factor. To fill this gap, we developed BioWolf, a highly wearable (40 mm 7 20 mm 7 2 mm) BCI platform based on Mr. Wolf, a parallel ultra low power system-on-chip featuring nine RISC-V cores with DSP-oriented instruction set extensions. BioWolf also integrates a commercial 8-channel medical-grade analog-to-digital converter, and an ARM-Cortex M4 microcontroller unit (MCU) with bluetooth low-energy connectivity. To demonstrate the capabilities of the system, we implemented and tested a BCI featuring canonical correlation analysis (CCA) of steady-state visual evoked potentials. The system achieves an average information transfer rate of 1.46 b/s (aligned with the state-of-the-art of bench-top systems). Thanks to the reduced power envelope of the digital computational platform, which consumes less than the analog front-end, the total power budget is just 6.31 mW, providing up to 38 h operation (65 mAh battery). To our knowledge, our design is the first to explore the significant energy boost of a parallel MCU with respect to single-core MCUs for CCA-based BCI

BioWolf: A Sub-10-mW 8-Channel Advanced Brain-Computer Interface Platform with a Nine-Core Processor and BLE Connectivity

Advancements in digital signal processing (DSP) and machine learning techniques have boosted the popularity of brain-computer interfaces (BCIs), where electroencephalography is a widely accepted method to enable intuitive human-machine interaction. Nevertheless, the evolution of such interfaces is currently hampered by the unavailability of embedded platforms capable of delivering the required computational power at high energy efficiency and allowing for a small and unobtrusive form factor. To fill this gap, we developed BioWolf, a highly wearable (40 mm 7 20 mm 7 2 mm) BCI platform based on Mr. Wolf, a parallel ultra low power system-on-chip featuring nine RISC-V cores with DSP-oriented instruction set extensions. BioWolf also integrates a commercial 8-channel medical-grade analog-to-digital converter, and an ARM-Cortex M4 microcontroller unit (MCU) with bluetooth low-energy connectivity. To demonstrate the capabilities of the system, we implemented and tested a BCI featuring canonical correlation analysis (CCA) of steady-state visual evoked potentials. The system achieves an average information transfer rate of 1.46 b/s (aligned with the state-of-the-art of bench-top systems). Thanks to the reduced power envelope of the digital computational platform, which consumes less than the analog front-end, the total power budget is just 6.31 mW, providing up to 38 h operation (65 mAh battery). To our knowledge, our design is the first to explore the significant energy boost of a parallel MCU with respect to single-core MCUs for CCA-based BCI

Estudo comparativo entre a agressividade superficial obtida na retificação com rebolos de óxido de alumínio e CBN, fabricados com ligantes resinóide e vitrificado

Este artigo apresenta um estudo da agressividade do rebolo (habilidade de corte). A retificação é um processo de usinagem preciso, o qual é amplamente usado na manufatura de componentes que requerem tolerâncias estreitas e superfícies bem acabadas. Na retificação, a ferramenta abrasiva é o rebolo, que é basicamente composto por núcleo, ligante e grãos abrasivos. As ferramentas testadas nessa pesquisa foram rebolos convencional (Al2O3) e superabrasivo (CBN). Entre os superabrasivos, três tipos específicos de ligantes foram testados: resinóide, vitrificado e resinóide de alto desempenho. Conseqüentemente, foi possível avaliar a habilidade de corte entre os diferentes tipos de rebolos testados. Assim, o rebolo convencional de óxido de alumínio apresentou a maior agressividade.This paper presents a study of the grinding wheel sharpness (cutting ability). Grinding is a precision machining process which is widely used in the manufacture of components requiring fine tolerances and smooth surfaces. In grinding, the abrasive tool is the grinding wheel, which is basically compounded by the core, the bond and the abrasive grains. The tools tested in this research were conventional (Al2O3) and superabrasive (CBN) grinding wheels. Among the superabrasive ones, three specific bond types were tested: resin, vitrified and high performance resin bond. Consequently, it was possible to evaluate the comparative cutting ability among the different types of grinding wheels tested. So the conventional wheel (Al2O3) presented the highest sharpness.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP

RF-powered low-energy sensor nodes for predictive maintenance in electromagnetically harsh industrial environments

This work describes the design, implementation, and validation of a wireless sensor network for predictive maintenance and remote monitoring in metal-rich, electromagnetically harsh environments. Energy is provided wirelessly at 2.45 GHz employing a system of three co-located active antennas designed with a conformal shape such that it can power, on-demand, sensor nodes located in non-line-of-sight (NLOS) and difficult-to-reach positions. This allows for eliminating the periodic battery replacement of the customized sensor nodes, which are designed to be compact, low-power, and robust. A measurement campaign has been conducted in a real scenario, i.e., the engine compartment of a car, assuming the exploitation of the system in the automotive field. Our work demonstrates that a one radio-frequency (RF) source (illuminator) with a maximum effective isotropic radiated power (EIRP) of 27 dBm is capable of transferring the energy of 4.8 mJ required to fully charge the sensor node in less than 170 s, in the worst case of 112-cm distance between illuminator and node (NLOS). We also show how, in the worst case, the transferred power allows the node to operate every 60 s, where operation includes sampling accelerometer data for 1 s, extracting statistical information, transmitting a 20-byte payload, and receiving a 3-byte acknowledgment using the extremely robust Long Range (LoRa) communication technology. The energy requirement for an active cycle is between 1.45 and 1.65 mJ, while sleep mode current consumption is less than 150 nA, allowing for achieving the targeted battery-free operation with duty cycles as high as 1.7%