KV
Korneel Van den Berghe
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2 records found
1
Journal article
(2025)
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Jason Yik, Korneel Van den Berghe, Douwe den Blanken, Younes Bouhadjar, Maxime Fabre, Aurora Micheli, Guido de Croon, Nergis Tömen, Charlotte Frenkel, More authors...
Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. This article presents NeuroBench, a benchmark framework for neuromorphic algorithms and systems, which is collaboratively designed from an open community of researchers across industry and academia. NeuroBench introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent and hardware-dependent settings. For latest project updates, visit the project website (neurobench.ai).
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Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. This article presents NeuroBench, a benchmark framework for neuromorphic algorithms and systems, which is collaboratively designed from an open community of researchers across industry and academia. NeuroBench introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent and hardware-dependent settings. For latest project updates, visit the project website (neurobench.ai).
Conference paper
(2025)
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B. Zhou, P.S.V. Sun, J. Yik, K. Van den Berghe, C. Frenkel, V. J. Reddi, A. Basu
Brain Machine Interfaces (BMI) that record signals from the motor cortex and translates these “thoughts” to action provides hope to paralyzed people. A high-accuracy decoder is needed for a seamless user experience. At the same time, it needs to be compact and low-power to support its integration in an implant to enable the compression required in wireless implantable BMIs. Hence, a model with a good trade-off between accuracy and resource requirement is desirable and was the subject of the 2024 Grand Challenge at BioCAS based on prerecorded datasets. However, in real-life, the usage of braincontrolled prosthetics, the result of decoding is presented to the user through visual feedback resulting in a closed-loop system. Hence, in the IEEE BioCAS 2025 conference, we organized the first grand challenge on Closed-Loop Neural Decoding (http://1.117.17.41/neural-decoding-grand-challenge/). The challenge requires users to move a cursor from a given start position to a target position based on spikes generated from a brain simulator. The evaluations were performed using the recently developed Neurobench software suite for benchmarking neuromorphic systems and the top 3 teams are invited to present their works in the IEEE BioCAS 2025.
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Brain Machine Interfaces (BMI) that record signals from the motor cortex and translates these “thoughts” to action provides hope to paralyzed people. A high-accuracy decoder is needed for a seamless user experience. At the same time, it needs to be compact and low-power to support its integration in an implant to enable the compression required in wireless implantable BMIs. Hence, a model with a good trade-off between accuracy and resource requirement is desirable and was the subject of the 2024 Grand Challenge at BioCAS based on prerecorded datasets. However, in real-life, the usage of braincontrolled prosthetics, the result of decoding is presented to the user through visual feedback resulting in a closed-loop system. Hence, in the IEEE BioCAS 2025 conference, we organized the first grand challenge on Closed-Loop Neural Decoding (http://1.117.17.41/neural-decoding-grand-challenge/). The challenge requires users to move a cursor from a given start position to a target position based on spikes generated from a brain simulator. The evaluations were performed using the recently developed Neurobench software suite for benchmarking neuromorphic systems and the top 3 teams are invited to present their works in the IEEE BioCAS 2025.