on-demand webinar

Oak Ridge National Lab (ORNL): A Spiking Neural Network Architecture for Ultra-low Power and Ultra-low Latency Computing

Estimated Watching Time: 23 minutes

Share

ORNL with the help of the Siemens EDA tools, including Catapult HLS, the neuromorphic accelerator is being adapted from an FPGA prototype to a more capable and lower-power ASIC implementation.

The U.S. Department of Energy (DOE) has called on the national labs to strengthen their microelectronics capabilities. In response, Oak Ridge National Lab (ORNL) has formed an internal microelectronics initiative with a focus on AI for scientific applications and energy-efficient computing. Researchers in the Architectures and Performance Group at ORNL have developed a digital neuromorphic architecture specifically designed for scientific applications that require ultra-low latency. With the help of the Siemens EDA tools, including Catapult HLS, the neuromorphic accelerator is being adapted from an FPGA prototype to a more capable and lower-power ASIC implementation. In collaboration with the Sensors and Electronics Group, the neuromorphic processor is also being re-target towards another import DoE mission of nuclear safeguards. This effort is exercising Siemens analog and mixed-signal tooling to integrate the neuromorphic processor with the sensing elements more closely by exploring efficient analog encoding neurons and event-driven gamma-ray detection. This presentation will give a brief overview of nuclear safeguards and other scientific applications at ORNL. Then, we will present the work done so far to develop the ASIC neuromorphic processor, the progress and plans for the mixed-signal processing and sensor subsystem, and finally, the dataset and algorithmic details we are leveraging to demonstrate the project’s capabilities.

Meet the speaker

Oak Ridge National Lab (ORNL)

Brett Witherspoon

Embedded Hardware and Software Engineer

Brett Witherspoon is an Embedded Systems Hardware & Software Engineer in the Sensors and Electronics Group at ORNL. He specializes in electronics design and analog/digital signal processing for low-power embedded systems. For much of his career, he has researched practical applications of AI/ML for wireless communications and sensing. Highlights include winning the DARPA Spectrum Challenge (2013-2014) and placing fourth in the final round of the DARPA Spectrum Collaboration Challenge (2016-2019) as an R&D engineer at Tennessee Technological University and later as an independent consultant. Since joining ORNL in 2021 he has focused primarily on radiation monitoring systems and embedded neuromorphic systems.

Related resources

Xperi®: A Designer’s Life with HLS
Webinar

Xperi®: A Designer’s Life with HLS

This webinar will discuss two aspects of their experience going from RTL to HLS. The first topic is using HLS for algorithms such as Face Detection th

BLUEDOT: Accelerating NN-based DeepField-PQO design using Catapult HLS
White Paper

BLUEDOT: Accelerating NN-based DeepField-PQO design using Catapult HLS

Service providers face high encoding costs due to HQ videos demand. BLUEDOT offers AI-based DeepField-PQO filter improving coding efficiency. It uses HLS for fast filter implementation into IP targeted for FPGA, Catapult for ASIC.

Cornell University: Building Sparse Linear Algebra Accelerators with HLS
Blog Post

Cornell University: Building Sparse Linear Algebra Accelerators with HLS

Sparse linear algebra (SLA) operations are essential in many applications such as data analytics, graph processing, machine learning, and scientific…