Greenwaves reveal their latest AI chip, GAP9. Just like the previous generation, it is aimed at AI inferencing in systems at the very edge of the network. Edge computing will increasingly become an integral part of the digital transformation phenomenon. The main benefits deriving from the use of these technologies are the reduction of processing latency, which allows real-time responses, and the saving of bandwidth, sending already processed and, therefore, smaller information to the data center. Compared to GreenWaves Technologies' currently shipping product, GAP8, the latest GAP9 reduces energy consumption by 5 times while enabling inference on neural networks 10 times larger.

GreenWaves Technologies GAP8 multi-core RISC-V microcontroller was introduced last year for artificial intelligence (AI) at the edge at ultra-low power consumption. The company has now expanded its GAP IoT application processor family with GAP9 that delivers five times lower power consumption compared to GAP8 microcontroller while enabling inference on neural networks 10 times larger. Greenwaves GAP9 will bring machine learning and signal processing capabilities to (coin cell) battery operated or energy harvesting devices such as IoT sensors in consumer and industrial markets, wearables, smart building, smart farming and so on. GAP9 is said to combine architectural enhancements with Global Foundries 22nm FDX process to achieve a peak cluster memory bandwidth of 41.6 GB/sec and up to 50 GOPS compute power while consuming only 50mW. The increased memory bandwidth (20x over GAP8) allows for greatly improved detection accuracy while analyzing streams of data from multiple different image sensors, microphones, and/or radar chips.

GreenWaves Technologies (GreenWaves), a fabless semiconductor start-up designing disruptive ultra-low-power AI embedded processors for battery-operated edge devices, announced today that it has been named a Gartner "Cool Vendor" based on the April 29, 2019 report "Cool Vendors in AI Semiconductors," authored by Alan Priestley, VP Analyst, and Saniye Alaybeyi, Senior Director Analyst. The report states, "[t]he deployment of products with AI capabilities continues to gain momentum but requires increasingly sophisticated semiconductor devices to enable this new generation of smart things. This report highlights three semiconductor vendors delivering innovative AI-enabled chips to facilitate this trend." GreenWaves Technologies, and its flagship GAP8 IoT application processor, is listed as one of these three vendors. "We believe that Gartner's Cool Vendor reports represent some of the most cutting-edge, innovative and pioneering brands and technologies. We also believe that to be included as a Cool Vendor in AI Semiconductors is a true testament to GAP8 and the company's commitment to delivering AI at the very edge," said Loic Lietar, GreenWaves' co-founder and CEO.

Researchers from the University of Bologna and the Swiss Federal Institute of Technology in Zurich (otherwise known as the ETH Zurich) claim to have engineered the world's smallest autonomous drone, according to a recent report. When it comes to the nano-drone industry, the primary focus of which is to build capable autonomous UAVs with the smallest, most lightweight batteries, this is a milestone. With a smaller battery comes a decrease in power and flight time. For nano-drones, which are defined by maximum four-inch diameters, developing a model somewhere in between these two poles has been a longtime struggle for engineers. The two teams of European researchers may have finally achieved that task, however, by reducing the power requirements of the drone by using a newly-developed processor that can efficiently run the autonomous, artificial intelligence-infused navigation required, according to Fast Company.

Flying in dynamic, urban, highly-populated environments represents an open problem in robotics. State-of-the-art (SoA) autonomous Unmanned Aerial Vehicles (UAVs) employ advanced computer vision techniques based on computationally expensive algorithms, such as Simultaneous Localization and Mapping (SLAM) or Convolutional Neural Networks (CNNs) to navigate in such environments. In the Internet-of-Things (IoT) era, nano-size UAVs capable of autonomous navigation would be extremely desirable as self-aware mobile IoT nodes. However, autonomous flight is considered unaffordable in the context of nano-scale UAVs, where the ultra-constrained power envelopes of tiny rotor-crafts limit the on-board computational capabilities to low-power microcontrollers. In this work, we present the first vertically integrated system for fully autonomous deep neural network-based navigation on nano-size UAVs. Our system is based on GAP8, a novel parallel ultra-low-power computing platform, and deployed on a 27 g commercial, open-source CrazyFlie 2.0 nano-quadrotor. We discuss a methodology and software mapping tools that enable the SoA CNN presented in [1] to be fully executed on-board within a strict 12 fps real-time constraint with no compromise in terms of flight results, while all processing is done with only 94 mW on average - 1% of the power envelope of the deployed nano-aircraft.