Researchers present NorthPole – a brain-inspired chip architecture that blends computation with memory to process data efficiently at low-energy costs. Since its inception, computing has been processor-centric, with memory separated from compute. However, shuttling large amounts of data between memory and compute comes at a high price in terms of both energy consumption and processing bandwidth and speed. This is particularly evident in the case of emerging and advanced real-time artificial intelligence (AI) applications like facial recognition, object detection, and behavior monitoring, which require fast access to vast amounts of data. As a result, most contemporary computer architectures are rapidly reaching physical and processing bottlenecks and risk becoming economically, technically, and environmentally unsustainable, given the growing energy costs involved. Inspired by the neural architecture of the organic brain, Dharmendra Modha and colleagues developed NorthPole – a neural inference architecture that intertwines compute with memory on a single chip. According to the authors, NorthPole “reimagines the interaction between compute and memory” by blending brain-inspired computing and semiconductor technology. It achieves higher performance, energy-efficiency, and area-efficiency compared to other comparable architectures, including those that use more advanced technology processes. And, because NorthPole is a digital system, it is not subject to the device noise and systemic biases and drifts that afflict analog systems. Modha et al. demonstrate NorthPole’s capabilities by testing it on the ResNet50 benchmark image classification network, where it achieved 25 times higher energy metric of frames per second (FPS) per watt, a 5 times higher space metric of FPS per transistor, and a 22 times lower time metric of latency relative to comparable technology. In a related Perspective, Subramanian Iyer and Vwani Roychowdhury discuss NorthPole’s advancements and limitations in greater detail.
Journal
Science
Article Title
Neural inference at the frontier of energy, space, and time
Article Publication Date
20-Oct-2023