Week In Review: IoT, Security, Auto


Internet of Things SiFive is bringing RISC-V to IoT makers and university developers through the RISC-V-based SiFive Learn Initiative, an open-source learning package that can be used to create a low-cost RISC-V hardware compatible with AWS IoT Core. The development platform SiFive Learn Inventor has a software package and education enablement course. It includes: The programmable SiFive Lear... » read more

A Trillion Security Risks


An explosion in IoT devices has significantly raised the security threat level for hardware and software, and it shows no sign of abating anytime soon. Sometime over the next decade the number of connected devices is expected to hit the 1 trillion mark. Expecting all of them to be secure is impossible, particularly as the attack surface widens and the attack vectors become more sophisticated... » read more

Power/Performance Bits: Dec. 3


Waking up IoT devices Researchers at UC San Diego developed an ultra-low power wake-up receiver chip that aims to reduce the power consumption of sensors, wearables, and Internet of Things devices that only need to communicate information periodically. "The problem now is that these devices do not know exactly when to synchronize with the network, so they periodically wake up to do this eve... » read more

Week In Review: IoT, Security, Autos


Internet of Things Amazon is expanding its IoT services. Alexa Voice Services will require less processing power on the device, moving from the 100MB of RAM and Arm Cortex A microprocessor to 1MB and an Arm Cortex-M. Amazon will do more of the processing in the cloud, enabling developers to add Alexa to smaller, single purpose devices. “It just opens up the what we call the real ambient inte... » read more

Power/Performance Bits: Nov. 25


Rigid or flexible in one device Researchers at the Korea Advanced Institute of Science and Technology (KAIST), Electronics and Telecommunications Research Institute (ETRI) in Daejeon, University of Colorado Boulder, Washington University in St. Louis, Cornell University, and Georgia Institute of Technology proposed a system that would allow electronics to transform from stiff devices to flexib... » read more

Week in Review: IoT, Security, Automotive


Connectivity, 5G Rambus has revealed a PCI Express 5.0 interface on advanced 7nm finFET process node for heterogenous computing aimed at performance-intensive uses, such as AI, data center, HPC, storage and 400GbE networking. With a PHY and a digital controller core recently acquired Northwest Logic, the interface has 32 GT/s (gigatransfers per second) bandwidth per lane with 128 GB/s bandwidt... » read more

GDDR6 Drilldown: Applications, Tradeoffs And Specs


Frank Ferro, senior director of product marketing for IP cores at Rambus, drills down on tradeoffs in choosing different DRAM versions, where GDDR6 fits into designs versus other types of DRAM, and how different memories are used in different vertical markets. » read more

Implementing Low-Power Machine Learning In Smart IoT Applications


By Pieter van der Wolf and Dmitry Zakharov Increasingly, machine learning (ML) is being used to build devices with advanced functionalities. These devices apply machine learning technology that has been trained to recognize certain complex patterns from data captured by one or more sensors, such as voice commands captured by a microphone, and then performs an appropriate action. For example,... » read more

Week in Review: Iot, Security, Automotive


IoT STMicroelectronics is now supporting LoRaWAN firmware updates over the air (FUOTA) in the STM32Cube ecosystem. Microsoft is adding ANSYS Twin Builder to its Microsoft Azure Digital Twins software, which companies use to create digital twins of machinery and IoT devices that are deployed in remotely. The digital replica of actual devices helps companies predict when maintenance is needed... » read more

Week in Review: IoT, Security, Autos


IoT/Edge Achronix teamed up with Bittware to develop a smart accelerator card based on a 7nm FPGA from Achronix. The card is targeted for edge devices, where pre-processing and acceleration of data movement is critical due to the enormous quantity of data being generated by sensors. The strategy is to move the processing closer to the data, rather than processing input from multiple sensors in... » read more

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