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Neuromorphic processors promise low-latency and energy-efficient processing by adopting novel brain-inspired design methodologies. Yet, current neuromorphic solutions still struggle to rival conventional deep learning accelerators' performance and area efficiency in practical applications. Event-driven data-flow processing and near/in-memory computing are the two dominant design trends of neuromorphic processors. However, there remain challenges in reducing the overhead of event-driven processing and increasing the mapping efficiency of near/in-memory computing, which directly impacts the performance and area efficiency. In this work, we discuss these challenges and present our exploration of optimizing event-based neural network inference on SENECA, a scalable and flexible neuromorphic architecture. To address the overhead of event-driven processing, we perform comprehensive design space exploration and propose spike-grouping to reduce the total energy and latency. Furthermore, we introduce the event-driven depth-first convolution to increase area efficiency and latency in convolutional neural networks (CNNs) on the neuromorphic processor. We benchmarked our optimized solution on keyword spotting, sensor fusion, digit recognition and high resolution object detection tasks. Compared with other state-of-the-art large-scale neuromorphic processors, our proposed optimizations result in a 6× to 300× improvement in energy efficiency, a 3× to 15× improvement in latency, and a 3× to 100× improvement in area efficiency. Our optimizations for event-based neural networks can be potentially generalized to a wide range of event-based neuromorphic processors. [ABSTRACT FROM AUTHOR]

Neuromorphic processors aim to emulate the biological principles of the brain to achieve high efficiency with low power consumption. However, the lack of flexibility in most neuromorphic architecture designs results in significant performance loss and inefficient memory usage when mapping various neural network algorithms. This paper proposes SENECA, a digital neuromorphic architecture that balances the trade-offs between flexibility and efficiency using a hierarchical-controlling system. A SENECA core contains two controllers, a flexible controller (RISC-V) and an optimized controller (Loop Buffer). This flexible computational pipeline allows for deploying efficient mapping for various neural networks, on-device learning, and pre-post processing algorithms. The hierarchical-controlling system introduced in SENECA makes it one of the most efficient neuromorphic processors, along with a higher level of programmability. This paper discusses the trade-offs in digital neuromorphic processor design, explains the SENECA architecture, and provides detailed experimental results when deploying various algorithms on the SENECA platform. The experimental results show that the proposed architecture improves energy and area efficiency and illustrates the effect of various trade-offs in algorithm design. A SENECA core consumes 0.47 mm2 when synthesized in the GF-22 nm technology node and consumes around 2.8 pJ per synaptic operation. SENECA architecture scales up by connectingmany cores with a network-on-chip. The SENECA platformand the tools used in this project are freely available for academic research upon request. [ABSTRACT FROM AUTHOR]