We are committed to provide our clients and partners universal, easy-to-use, efficient, scalable, flexible and lowest power FPGA based machine learning inference platforms. Our AIScale architecture in combination with our DeepCompressor serves clients in the fields of computer vision, robotics, speech recognition, surveillance systems as well as data centers. Neural network acceleration from edge- to server devices.

Kortiq´s novel way of mapping calculations to hardware resources in combination with highly advanced compression methods, which offer a significant reduction in required external memory transfer size and power, enable our clients in the above industries to achieve fast turnaround from idea to product, with having an efficient and economic solution in mind.

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CNNs: AlexNet, VGG-16, Yolo-Tiny and KortiqY3

KY3 CNN Total # of Parameters: 3.946.416
KY3 CNN Total Number of Operations per Input Image: 428.603.392
AlexNet CNN Total # of Parameters: 60.963.848
AlexNet CNN Total # of Operations/Input Image: 725.508.992
VGG-16 CNN Total # of Parameters: 138.353.320
VGG-16 CNN Total # of Operations/Input Image: 15.476.385.792
YOLO-Tiny CNN Total # of Parameters: 15.855.536
YOLO-Tiny CNN Total # of Operations/Input Image: 3.491.231.744



FIRST: Initialize reconfigurable structure

Use a dedicated IF fuction to initialize the network.

Your network can be any CNN e.g. ResNet, AlexNet, Tiny Yolo, VGG16 …

AIScale will be configured based on pre-trained network models using TensorFlow, AIScale DeepCompressor and AIScale TF2AIScale Translator.

No need to generate different hardware architectures per CNN

No need for SW programming (C, C++, OpenCL)

No need to learn how to use specific libraries

No need to learn which functions to use with what parameters



SeconD: RUN the NETwork

Once configured and initialized, the AIScale accelerator will act as







based on the chosen network structure. Activation functions are executed as a post-processing step of each layer

VIDEO – Kortiq Small and Efficient CNN Accelerator: Powered by Xilinx


Kortiq provides an easy to use, scalable and small form factor CNN accelerator. The device supports all types of CNN and dynamically accelerates different layer types found in the network. The Xilinx Zynq family of SoCs and MPSoCs help Kortiq devices achieve targeted performance levels and flexibility, while being cost-effective.

AIScale CC (MAC)

The Re-configurable Compute Core is the heart of our AI Scale accelerator and provides exceeding flexibility and scalability. The small footprint is based on coarse-grained true re-configurable computing principle and architecture.

AIScale CC supports and processes Convolutional-, Pooling-, Adding- and Fully-Connected layers. Based on your needs in size, frames per second or accuracy the accelerator can be parameterized from very few CC to several 100 CC.

Make advantage of a hardwired, optimized network with opportunity to switch between different CNN solutions based on customers needs using pre-trained network parameters. It can be structured for low latency and custom memory allocations

Colleague Classification @ 27fps with AIScale Hardware Accelerator IP using 32 Compute Cores @ 120 MHz with our KortiqY3 network.
This can e.g. be implemented in a cost optimized Zynq device.

AIScale DeepCompressor

Tensorflow2AIScale Translator

AIScale CNN Hardware Accelerator IP

Download Data Sheet

Please register here to download your copy of the AIScaleCDP2 IP Core (Preliminary Datasheet)
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Gebrüder-Eicher-Ring 45
85659 Forstern, Germany

Phone: +49 8124 91890 03
Fax: +49 8124 91890 55

Geschäftsführer: Ullrich Nake, Harald Weiss

Commercial Register B München: HRB 226267
VAT-IdNr.: DE306907359

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