Ultra96™ is an Arm-based, Xilinx Zynq UltraScale+™ MPSoC development board based on the Linaro 96Boards specification. The 96Boards’ specifications are open and define a standard board layout for development platforms that can be used by software application, hardware device, kernel, and other system software developers. Ultra96 represents a unique position in the 96Boards community with a wide range of potential peripherals and acceleration engines in the programmable logic that is not available from other offerings. Product Brief (Datasheet)
Ultra96 provides four user-controllable LEDs. Engineers may also interact with the board through the 96Boards-compatible low-speed and high-speed expansion connectors by adding peripheral accessories such as those included in Seeed Studio’s Grove Starter Kit for 96Boards.
Micron LPDDR4 memory provides 2 GB of RAM in a 512M x 32 configuration. Wireless options include 802.11b/g/n Wi-Fi and Bluetooth 4.2 (provides both Bluetooth Classic and Low Energy (BLE)). UARTs are accessible on a header as well as through the expansion connector. JTAG is available through a header (external USB-JTAG required). I2C is available through the expansion connector.
Ultra96 provides one upstream (device) and two downstream (host) USB 3.0 connections. A USB 2.0 downstream (host) interface is provided on the high speed expansion bus. Two Microchip USB3320 USB 2.0 ULPI Transceivers and one Microchip USB5744 4-Port SS/HS USB Controller Hub are specified.
The integrated power supply generates all on-board voltages from an external 12V supply (available as an accessory).
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- Xilinx Zynq UltraScale+ MPSoC ZU3EG SBVA484
- Micron 2 GB (512M x32) LPDDR4 Memory
- Delkin 16 GB MicroSD card + adapter
- Pre-loaded with PetaLinux environment
- Wi-Fi / Bluetooth
- Mini DisplayPort (MiniDP or mDP)
- 1x USB 3.0 Type Micro-B upstream port
- 2x USB 3.0, 1x USB 2.0 Type A downstream ports
- 40-pin 96Boards Low-speed expansion header
- 60-pin 96Boards High speed expansion header
- 85mm x 54mm form factor
- Linaro 96Boards Consumer Edition compatible
- Artificial Intelligence
- Machine Learning
- IoT/Cloud connectivity for add-on sensors
- Ultra96 development board
- 16 GB pre-loaded MicroSD card + adapter
- Voucher for SDSoC license from Xilinx
- Quick-start instruction card
Optional Add-on Items
- External 2.0A @ 12V power supply
- External 4.0A @ 12V power supply
- USB-to-JTAG/UART pod
- Ultra96-V2 Click Mezzanine Board
- Seeed Studios Grove Starter Kit for 96Boards
- Compatible Accessories
Other Qualified microSD Cards
- Delkin Utility MLC 128 GB microSD Card
- Delkin Utility MLC 64 GB microSD Card
- Delkin Utility MLC 16 GB microSD Card
- Delkin Utility MLC 8 GB microSD Card
Introduction to Ultra96
These tutorials provide a means to integrate several different technologies on a single platform. Using the Avnet target boards, we have the power of a ARM Cortex-A9 processors, combined with the unrivaled flexibility of Xilinx programmable logic to implement custom hardware systems. We use a Linux kernel as the foundation operating system running on the processor cores which enables a very large ecosystem of software to be run on our development kits. Virtual machines can provide a very convenient Ubuntu development environment for building the hardware platform and cross-compiling software to target the Processing System.
Accelerate your designs with PYNQ, a Python friendly development framework for the ZYNQ SoC family. Available now for Ultra96.
This page provides an introduction to the "Accelerated Image Classification via Binary Neural Network" (short AIC) design example. This design example demonstrates how moving software implemented neural networks can be dramatically accelerated via Programmable Logic. In this design a Binary Neural Network (BNN) is implemented. Depending on silicon platform an acceleration of 6,000 to 8,000 times is demonstrated. Via the graphical user interface the user can see metrics, images and classification results.
DNNDK™ (Deep Neural Network Development Kit) - DeePhi™ deep learning SDK, is designed as an integrated framework, which aims to simplify & accelerate DL (Deep Learning) applications development and deployment on DeePhi DPU™ (Deep Learning Processing Unit) platform.