RK3566 and RK3568 are the latest AIoT processors from Rockchip equipped with four Arm Cortex-A55 cores, a Mali-G52 GPU, and an impressive range of peripherals that make them suitable for all sorts of applications including AI edge computing and network video recorder (NVR). We already know Pine64 is working on Quartz64 SBC based on Rockchip RK3566, but I’ve just noticed Geniatech’s “RK3568 development board” that should offer even more features since the processor comes with extra I/Os compared to its little brother. Let’s have a look. Geniatech RK3568 development board specifications: SoC – Rockchip RK3568 quad-core Cortex-A55 processor up to 2.0 GHz with Arm Mali-G52 GPU supporting OpenGL ES 1.1/2.0/3.2, OpenCL 2.0, Vulkan 1.1, 0.8 TOPS NPU for AI acceleration System Memory – Up to 4 GB LPDDR4 Storage – Up to 128 GB eMMC 5.1 flash, 1x SATA 3.0 port, MicroSD card slot, M.2 socket for NVMe SSD (See expansion section) Video Output 1x HDMI2.1 up to 4096 x […]
We’ve had our fair share of low-cost x86 SBCs in recent years, mostly based on Intel Cherry Trail processor, with the likes of Atomic Pi and Rock Pi X. Ice Whale Technology has come up with its own low-cost Intel single board computer with ZimaBoard “single board server” as it mostly has network storage applications in mind, but with a 4K capable mini DisplayPort connector it could also be used as a standard SBC or mini PC. There are two versions of the board both with Apollo Lake processors: ZimaBoard 216 with a dual-core Celeron N3350 coupled with 2GBRAM and 16GB eMMC flash, and ZimaBoard 832 powered by a quad-core Celeron N3450 with 8GB RAM and 32GB storage. Both ZimaBoard SBC shares most of the same specifications in terms of I/Os: SoC – ZimaBoard 216 – Intel Celeron N3350 dual-core processor @ 1.1/2.4GHz (Burst freq.) with 12 EU Intel HD graphics 500 @ 200 MHz / 650 MHz (Burst freq.); […]
The Raspberry Pi 4 is a pretty cool board, but if you wished it was just a bit smaller, and you could use the PCIe interface exposed by the Broadcom BCM2711 processor more easily, Timon has designed Piunora carrier board for the Raspberry Pi Compute Module 4. The solution provides a board with the guts of Raspberry Pi 4 SBC but using the Arduino form factor including access to the six ADC pins, and an M.2 socket with the PCIe signal from the Broadcom SoC. Piunora carrier board (preliminary)specifications: SoM compatibility – Raspberry Pi CM4 module with Broadcom BCM2711 quad-core Cortex-A72 processor @ 1.5 GHz, 1 to 8GB RAM, optional 4GB to 32GB eMMC flash, optional wireless module with 802.11b/g/n/ac WiFi 5 and Bluetooth 5.0 Storage – 1x MicroSD card socket (for the OS when using Raspberry Pi CM4Lite system-on-module) Video Output – 1x HDMI 2.0 port up to 4Kp60 Camera I/F – MIPI camera connector USB – 1x USB […]
We first covered Rockchip RK3566 processor in February, but we’ve been informed the specifications have changed since then, and earlier this month, we also wrote about RK3568 quad-core Cortex-A55 processor with the same base features but some extra I/Os. But due to NDAs, people familiar with the chips could not release too many details, so we did not get the full picture. This has just changed as the datasheets for both processors have just dropped from somewhere here and there, and we can get a much closer look. I’ve highlighted the obvious differences above, with the big ones being the PCIe interface and RK3568 having dual Gigabit Ethernet. The other features comparison can be found in the table below. Important note. Rockchip RK3566 and RK3568 are using Multi-PHY Interfaces, and while the table above lists all possible interfaces, they can not all be used at the same time. PCIe, SATA, USB 3.0 are multiplexed in most cases as shown in […]
Rockchip RK3588 is one of the most anticipated processors for the year on this side of the Internet with the octa-core processor features four Cortex-A76 cores, four Cortex-A55 cores, an NPU, and 8K video decoding support. The roadmap shows an expected launch date in Q3/Q4 2020, but sadly the release date will be pushed back in the future. Having said that, the Rockchip Developer Conference (RKDC) is now taking place, and the company has put up a poster that reveals a bit more about the processor. That means we now have more detailed Rockchip RK3588 specifications: CPU – 4x Cortex-A76 and 4x Cortex-A55 cores in dynamIQ configuration GPU – Arm Mali “Odin” MP4 GPU AI Accelerator – 6 TOPS NPU 3.0 (Neural Processing Unit) VPU – 8Kp60 video decoding support, 8Kp30 encoding support Memory I/F – LPDDR4x/LPDDR5 up to 32GB Storage – eMMC 5.1, SDIO, SATA 3.0 (multiplexed with PCIe 2.0) Video Output Dual HDMI 2.1 / eDP up to […]
Last week, Mythic announced a breakthrough with compute-in-memory technology based on a 40 nm process with what the company claims to be the industry’s first Analog Matrix Processor. The M1108 AMP AI accelerator chip targets high-end edge AI applications including smart home, AR/VR, drones, and is said to set a benchmark in the industry for high performance and low power in a single cost-effective device, also available in M.2 and PCIe form factors. The M1108 comes with an array of flash cells, ADCs, a 32-bit RISC-V nano-processor, a SIMD vector engine, SRAM, and a high-throughput Network-on-Chip (NOC) router. With 108 AMP tiles, the M1108 provides up to 35 Trillion-Operations-per-Second (TOPS) enabling ResNet-50 at up to 870 fps. This enables a power-efficient execution of complex AI models such as ResNet-50, YOLOv3, and OpenPose Body25. The industry leader NVIDIA also has a similar AI accelerator chip NVIDIA Xavier AGX which delivers up to 32 TOPS. A wide variety of host processors are […]
Last week, Flex Logix announced the InferX X1 AI Inference Accelerator at Linley Fall Conference 2020. Today, they announced the InferX X1 SDK, PCIe board, and M.2 board. InferX X1 Edge Inference SDK The InferX Edge Inference SDK is simple and easy. The input to the compiler can be an open-source high-level, hardware-agnostic implementation of the neural network model that can be TensorFlow Lite or ONNX model. The compiler takes this model and looks for the available X1 resources and generates a binary executable file. This goes to the runtime which then takes the input stream, for example, a live feed from a camera. The user has to specify which compiler model, then the InferX X1 driver takes it and sends it to hardware. The binary file generated is fed to InferX X1 through the runtime. Then it takes the input data stream with a user-specified model and gives the output back to the host. This gives an advantage to […]
[Update Sep 3, 2020: The post has been edited to correct Google Coral M.2 power consumption] If you were to add M.2 or mPCIe AI accelerator card to a computer or board, you’d mostly have the choice between Google Coral M.2 or mini PCIe card based on the 4TOPS Google Edge TPU, or one of AAEON AI Core cards based on Intel Movidius Myriad 2 (100 GOPS) or Myriad X (1 TOPS per chip). There are also some other cards like Kneron 520 powered M.2 or mPCIe cards, but I believe the Intel and Google cards are the most commonly used. If you ever need more performance, you’d have to connect cards with multiple Edge or Movidius accelerators or use one M.2 or mini PCIe card equipped with Halio-8 NPU delivering a whopping 26 TOPS on a single chip. Hailo-8 M.2 accelerator card key features and specifications: AI Processor – 26 TOPS Hailo-8 with 3TOPS/W efficiency, and using a proprietary […]
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