Renesas RZ/G2L MPUs Feature Cortex-A55 & Cortex-M33 Cores for AI Applications

Renesas Electronics Corporation announced RZ/G2L MPUs, allowing enhanced processing for an extensive variety of AI applications. The RZ/G2L group of 64-bit MPUs includes three new MPU models featuring Arm Cortex-A55, and an optional Cortex-M33 core. These are RZ/G2L, RZ/G2LC, and RZ/G2UL MPUs. The Cortex-A55 CPU core typically delivers approximately 20 percent improved processing performance compared with the previous Cortex-A53 core, and according to Renesas, is around six times faster in “essential processing for AI applications”. The company already has four mid to high-end design level MPUs including RZ/G2E, RZ/G2N, RZ/G2M, and RZ/G2H, with combinations of Cortex-A53 and Cortex-A57 cores. The new RZ/G2L group of three MPUs forms the entry-level design with Cortex-A55. Hence, the seven MPU models together provide scalability from entry-level to high-end design. Common Key Features in RZ/G2L, RZ/G2LC, and RZ/G2UL MPUs Up to 2x Cortex-A55 cores Cortex-M33 core Camera interface (MIPI-CSI) Display interface (Parallel-IF) USB2.0 interface 2ch, SD interface 2ch CAN interface (CAN-FD) 2x Gigabit Ethernet DDR4 […]

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MicroMod modular ecosystem offers M.2 microcontrollers cards and carrier boards

MicroMod is a modular interface ecosystem for quick embedded development and prototyping. MicroMod comes with two components, that is a microcontroller “processor board” and a carrier board. PC industry’s M.2 connector is the interface between these two components. The carrier boards are for the usage of various peripherals and the processor board act as the brain of the application system.  MicroMod processor board has a dimension of 22×22 mm that can be easily fitted on the carrier boards. Although, the original M.2 standard was dedicated to swapping out peripherals where a user could swap one component with the other one. The MicroMod standard is for swapping out microcontrollers according to the functional and application requirements.  MicroMod Processor Boards Artemis Processor Board comes with an Ambiq Apollo 3 Blue Arm Cortex-M4F with BLE 5.0 running up to 96MHz and a power rating of less than 5mW. It also supports the TensorFlow Lite framework. ESP32 Processor Board comes with a dual-core Tensilica […]

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Voltage Measurement HAT for Raspberry Pi offers 16-bit analog inputs

MCC has launched the MCC 128 voltage measurement DAQ HAT for Raspberry Pi for data acquisition and data logging systems. It includes 8 analog inputs with 16-bit resolution for a range of -10V to +10 V at the data rate of 100 kS/s. This data rate can be increased by stacking up to 8 HATs for 64 channels of data which can produce a faster data rate up to 320 kS/s. The MCC 128 DAQ HAT is compatible with all Raspberry Pi models with the 40-pin GPIO header, excluding the original Pi 1 A or B with the 26-pin header. It is recommended to use the SPI interface for connecting LCD displays using the GPIO header. The configuration parameters of the board are stored in the EEPROM to allow automatic set up of GPIO pins to Raspberry Pi after the connection of the device. Key Features of MCC 128 DAQ HAT 16-bit, 100 kS/s A/D converter Single-ended and differential input […]

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Mini-ITX SBC with Tiger Lake UP3 CPU offers dual 2.5GbE, quad display support

IEI launched tKINO-ULT6 is a Thin Mini-ITX SBC that comes with an 11th generation Intel core processor part of the Tiger Lake-UP3 CPU family. The tKINO-ULT6 Mini-ITX SBC also supports quadruple independent displays, SATA 6Gb/s, dual Intel 2.5GbE, HD audio, and RoHS. It can handle a wide range of input DC power from 9V to 36 V. The tKINO-ULT6 SBC is expected to be found in CNC (Computer Numerical Control) machines, real-time controls, human-machine interfaces, tool applications, medical imaging, and other applications requiring high resolution. Key Features of  the tKINO-ULT6 Mini-ITX SBC Mini-ITX form factor with 11th Gen. Intel Tiger Lake UP3 Embedded processor, support for DDR4-3200 memory Video Output – Four independent displays via HDMI, DP, eDP/LVDS, USB4 Networking Dual Intel 2.5GbE LAN M.2 A key for WIFI+Bluetooth (PCIe x1/USB 2.0), M.2 M key for Storage (PCIe x2/SATA) Expansion – PCIe x8 Gen 4.0 slot  The tKINO-ULT6 Mini-ITX SBC supports up to 64GB DDR4-3200, dual SATA III connection with […]

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DBM10 AI SoC is optimized for battery-powered voice and sensor processing

DSP Group announced DBM10 a low-power AI/ML-enabled dual-core SoC. The SoC is equipped with a DSP (Digital Signal Processor) and a dedicated nNetLite NN (Neural Network) processor that improves voice and sensor processing and ensures low-power consumption when working with sufficient-sized neural networks. Key Specifications of NN Processor Form factor: ~4 mm2  Ultra-low-power inference consumption – ~500 µW (typical) for voice NN algorithms Runs Hello Edge 30-word detection model @ 1 MHz (125 MHz available) Allows porting of large models (10s of megabytes) without significant accuracy loss using model optimization and compression. DBM10 AI SoC uses the combined functioning of machine learning, voice, and sensor parameters. This includes voice trigger (VT), voice authentication (VA), voice command (VC), noise reduction (NR), acoustic echo cancellation (AEC), sound event detection (SED), proximity and gesture detection, sensor data processing, and equalization. The DBM10 is suitable for battery-operated devices like smartphones, tablets, and wearables. It is even suitable for true wireless stereo (TWS) headsets, and […]

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STM32WB5MMG Wireless Module simplifies Bluetooth LE, Zigbee, OpenThread connectivity

The STM32WB5MMG (STM32) is a wireless microcontroller module by STMicroelectronics. It is a compact ultra-low-power module that allows customers to design 2-layer PCBs and integrates everything up to the antenna, including an IPD (integrated passive device) for reliable antenna matching in order to reduce the overall costs. The STM32 wireless module is compatible with BLE (Bluetooth Low Energy) 5.0, OpenThread, Zigbee 3.0, dynamic and static concurrent modes, and 802.15.4 proprietary protocols. It also supports simultaneous dual-protocol mode that allows IEEE 802.15.4 radio-based protocols like Zigbee 3.0 and OpenThread for direct connection with any BLE device. Overview of STM32 Wireless Module The STM32 wireless module is a SiP-LGA86 package (System in Package Land Grid Array)  with various external components including:  STMicro STM32WB55 Cortex-M4/M0+ wireless MCU LSE crystal  HSE crystal Passive components for SMPS  Antenna matching and antenna  IPD for RF matching and harmonics rejection Key Features of STM32 Wireless Module Dedicated Arm Cortex-M0+ for radio and security tasks Dedicated Arm Cortex-M4 […]

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Adafruit Voice Bonnet is meant for DIY Raspberry Pi Smart Speakers

Adafruit Voice Bonnet features two speakers and two mics, that can be used as an audio-voice interface for Raspberry Pi SBC to create a DIY smart speaker or other audio product. The voice bonnet can work with any Raspberry Pi from Pi Zero up to Pi 4, with 40-pin 2 x 20 connector. Two speaker outputs of the voice bonnet have a power rating of 1 Watt. The voice bonnet contains 3.5 mm stereo outputs, headphone stereo, or line-out audio. The Adafruit voice bonnet has an on-board WM8960 low-power stereo codec that uses I2S digital audio for both input and output. The WM8960 codec has a dual analog input, it consists of a left mic and a right mic. The codec integrates a complete microphone interface and a stereo headphone driver. Adafruit says “For DIY speakers, solder any 1W+ speaker to one of these JST 2-PH cables. If you’d like to stack another HAT or bonnet on top, use a […]

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BHI260AP is an AI smart sensor with built-in IMU by Bosch Sensortec

BHI260AP AI smart sensor integrates a 6-axis IMU, a 32-bit customizable programmable microcontroller, and various software functionalities. The AI smart sensor has embedded AI with on-sensor applications such as fitness tracking, navigation, machine learning analytics, and orientation estimation. The dimensions of the miniaturized AI smart sensor are 4.1mm x 3.6mm x 0.83 mm. Hardware Features of BHI260AP AI Smart Sensor ARC EM4 CPU includes ARCv2 16/32 bit instruction set working up to a frequency of 3.6 MHz. The core also integrates Floating Point Unit (FPU) and Memory Protection Unit (MPU) with 4 channel micro DMA controller. CPU has two modes of AI functioning at 25Hz and 50Hz with 249µA and 386µA of current consumption respectively. Integrated sensor (6-DoF IMU) includes 16-bit 3 axis accelerometer and 16-bit 3 axis gyroscope. The sensor works at an operating voltage of 1.8 V with a standby current value of 8µA, hence the power consumption is only 0.014 mW in standby mode. Self Learning AI […]

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