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Abstract 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). 

Neuromorphic vision sensors (NVS), also known as silicon retina, capture aspects of the biological functionality of the mammalian retina by transducing incident photocurrent into an asynchronous stream of spikes that denote positive and negative changes in intensity. Current state-of-the-art devices are effectively leveraged in a variety of settings, but still suffer from distinct disadvantages as they are transitioned into high performance environments, such as space and autonomy. This paper provides an outline and demonstration of a data synthesis tool that gleans characteristics from the retina and allows the user to not only convert traditional video into neuromorphic data, but characterize design tradeoffs and inform future endeavors. Our retinomorphic model, RetinoSim, incorporates aspects of current NVS to allow for accurate data conversion while providing biologically-inspired features to improve upon this baseline. RetinoSim was implemented in MATLAB with a Graphical User Interface frontend to allow for expeditious video conversion and architecture exploration. We demonstrate that the tool can be used for real-time conversion for sparse event streams, exploration of frontend configurations, and duplication of existing event datasets.