Like the genie in Aladdin, KORTIQ’s FPGA-based AIScale CNN Accelerator takes pre-trained CNNs (convolutional neural networks)—including industry standards such as ResNet, AlexNet, Tiny Yolo, and VGG-16—compresses them, and fits them into Xilinx’s full range of programmable logic fabrics. Devices such as the Zynq SoC and Zynq UltraScale+ MPSoC have multiple on-chip processors that can provide data to the AIScale CNN Accelerator instantiated in the FPGA fabric and accept its classification output, enabling designs such as single-chip, intelligent industrial or surveillance video cameras.
KORTIQ’s AIScale DeepCompressor compresses the trained CNN and outputs a resulting description file that represents the trained CNN. KORTIQ’s TensorFlow2AIScale translator then prepares the compressed CNN for use with KORTIQ’s AIScale RCC (reconfigurable compute core) IP that performs real-time recognition based on the trained CNN. Because the compressed CNN takes the form of a relatively small description, many such description files can be stored in on- or off-chip memory, making fast switching among trained CNNs quite feasible. Currently, KORTIQ is focusing on embedded vision and computer vision applications such as image classification, object recognition, object tracking, and face recognition.
Here’s a conceptual block diagram of the KORTIQ offering:
The hardware portion of this product, the AIScale RCC, is a coarse-grained, scalable, accelerator that can be instantiated in programmable logic—for example in the FPGA fabric of a Zynq Z-7020 SoC for small-footprint instances of the AIScale RCC. Larger All Programmable devices such as larger Zynq SoCs and Zynq UltraScale+ MPSoCs can implement more processing blocks within the accelerator core, which in turn makes the accelerator go even faster. You can use this feature to scale system performance up by picking devices with larger FPGA arrays or reducing power consumption by picking smaller devices.
For more information about the AIScale product family, contact KORTIQ directly.