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).

To give paralyzed people hope for a normal life, Brain Machine Interfaces (BMI) record signals from the motor cortex and a decoder translates these 'thoughts' to action. 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. In the IEEE BioCAS 2024 conference, we organized the first grand challenge on neural decoding for motor control. The evaluations were performed using the recently developed Neurobench software suite for benchmarking neuromorphic systems. There were two tracks -one preferring solutions with highest accuracy while the other gave weightage to the tradeoff between accuracy and implementation complexity. Out of the 10 teams registered for this event, the top 3 teams are invited to present their works in the IEEE BioCAS 2024.