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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., Computer Engineering

With recent breakthroughs in AI (Artificial Intelligence) technology, the impact of AI on society can be felt in various fields. The market for AI software, for example, reached a valuation of \$62 billion in 2022. A growing number of new computer architectures specialized in running these AI software were also developed. At first they were run on conventional CPUs (Central Processing Unit) and GPUs (Graphical Processing Units), but then more specialized hardware emerged, such as the TPU (Tensor Processing Unit). However, since algorithms in these AI software are generally data-intensive, the power consumption became a problem. Therefore, as many of these algorithms were based on biological neural networks, there is a growing interest to develop hardware similarly based on principles found these networks as well to replicate their efficiency. This new architecture is known as neuromorphic architecture. However, a new architecture does not come without challenges. As a nascent and fragmented field, neuromorphic computing in general lacks a standardized benchmarking suite or methodology. In other, more mature fields, benchmarks are a standard way of evaluating the performance of different designs objectively and fairly. This thesis aims to propose and demonstrate a benchmarking methodology and implementation flow for neuromorphic processors. This methodology aims to measure the important performance metrics for a neuromorphic processor, both on the small scale of individual synaptic operations, and the large scale of performing an actual workload. The chosen workload is a keyword spotting program based on a simple DNN architecture, which detects a specific phrase in an audio recording. This workload was chosen due to its potential application in an environment where energy is limited, such as an embedded device. The neuromorphic processor that is the target of this benchmarking is SENeCA (short for Scalable Energy-efficient Neuromorphic Computer Arc, Electrical Engineering | Embedded Systems