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Award Data

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The Award database is continually updated throughout the year. As a result, data for FY20 is not expected to be complete until September, 2021.

Download all SBIR.gov award data either with award abstracts (290MB) or without award abstracts (65MB). A data dictionary and additional information is located on the Data Resource Page. Files are refreshed monthly.

  1. High Acceleration and Hypervelocity Inertial Measurement Unit

    SBC: EngeniusMicro, LLC            Topic: OSD181001

    Gun-launched applications currently expose inertial measurement units (IMUs) to harsh acceleration, shock, and vibration environments. Furthermore, as they become smarter, they present tighter constraints on size, weight, power, and cost (SWaP-C), while still requiring high levels of performance. New accelerometer technology must reduce SWaP-C while operating through high-g acceleration environmen ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  2. High Acceleration and Hypervelocity Inertial Measurement Unit

    SBC: Quality Support, Inc.            Topic: OSD181001

    OSD solicits vendors for a ‘High Acceleration and Hypervelocity Inertial Measurement Unit System’ for use in 155mm projectiles. Development of a gun-hardened and highly miniaturized IMU would revolutionize the way in which 155mm gun systems are employed. A 155mm projectile with guidance, navigation and control would enable the development of new systems such as precision gun launched missile d ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  3. Electro-optical Seeker

    SBC: Polaris Sensor Technologies, Inc.            Topic: OSD181002

    Mission times for high velocity projectiles (HVP) are very short and detection and discrimination of targets must happen quickly and decisively. One way to achieve this is through the enhanced contrast resulting from polarized sensing, which tends to highlight manmade objects and suppress natural background clutter. Thermal polarimetric sensing in a small package has been demonstrated already but ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  4. Scalable Low-Cost AESA Transmitter with Phase-Only Nulling

    SBC: Parry Labs, LLC            Topic: SCO182002

    Parry Labs proposes the development and fabrication of a scalable, low-cost, transmit only wideband active electronically scanned array (AESA) operating in Ku band. Operating at Ku band maximizes aperture gain while also minimizing physical size and transmission loss due to rain and other atmospheric effects. The proposed system will be based on a scalable tile building block that can be used to c ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  5. Few-shot Object detection via Reinforcement Control of Image Simulation (FORCIS)

    SBC: Expedition Technology, Inc.            Topic: SCO182006

    Few-shot Object detection via Reinforcement Control of Image Simulation (FORCIS) will combine deep reinforcement learning with additional training data augmentations and strategies to develop robust few shot detectors leveraging available simulations.

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  6. Deep Reinforcement Learning for Model Training with Simulated Imagery/Next Century Corporation

    SBC: Next Century Corporation            Topic: SCO182006

    Next Century Corporation proposes the development of the Computer Vision Synthetic Image Generation Harness for Training and Testing (CV SIGHTT), a prototype system that learns to train a generation-based image recognition system for low-shot detection in the remote sensing domain. CV SIGHTT will use Deep Evolution Strategies to select the procedure for synthetic image generation, choosing the one ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  7. Analog-Digital Hybrid ASIC Implementation of Miniaturized Neural Nets/Portmanteau Industries, LLC

    SBC: Portmanteau Industries, LLC            Topic: SCO182007

    The vision of truly autonomous, smart, and powerful edge devices requires the ability to perform intensive machine learning tasks with limited size, weight, and power budgets. We propose a highly efficient ASIC design that enables edge devices to achieve this vision.

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  8. A High-Throughput and Energy-Efficient Hardware Processor for Vision-based Object Detection Systems/Intelligent Automation, Inc.

    SBC: INTELLIGENT AUTOMATION, INC.            Topic: SCO182007

    We propose to design and implement a high-throughput and energy-efficient FPGA-based hardware processor (accelerator) for the deployment of Convolutional Neural Network (CNN) architectures at real-time embedded and resource-bound environments that have low size, weight, power and cost (SWaP-C) requirements. CNNs have been shown tremendous success in vision-based object detection tasks. Two differe ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  9. REMIX: REconfigurable Machine Intelligence eXtreme compute architecture/Intelligent Computing Machines, LLC

    SBC: Intelligent Computing Machines, LLC            Topic: SCO182007

    We propose the analysis of a proposed architecture called ReMIX: Reconfigurable Machine Intelligence eXtreme compute architecture. This design is a chip multiprocessor with a non-von Neumann architecture and two layers of Network on Chip that enables low-power, high speed, parallel computations with very high throughput. Rather than have data traverse the memory hierarchy as is typical in von Neum ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  10. QGAN: Quantum Generative Adversarial Network to Secure Deep Learning

    SBC: INTELLIGENT AUTOMATION, INC.            Topic: SCO183001

    Despite deep neural networks have demonstrated tremendous success in various commercial and DoD applications, they are susceptible to adversarial attacks with detrimental outcomes to the underlying applications. The generative adversarial network (GAN) provides a good way of defending against adversarial learning attacks, but it is faced with a practical challenge, as classical computers are not a ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
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