Reducing Smart Phone Environmental Footprints with In-Memory Processing

Smart phones have revolutionized the availability of computing to the consumer. Recently, smart phones have been aggressively integrating artificial intelligence (AI) capabilities into their devices. The custom designed processors for the latest phones integrate incredibly capable and energy efficient graphics processors (GPUs) and tensor processors (TPUs) to accommodate this emerging AI workload and on-device inference. Unfortunately, smart phones are far from sustainable and have a substantial carbon footprint that continues to be dominated by environmental impacts from their manufacture and far less so by the energy required to power their operation. In this paper we explore the possibility of reversing the trend to increase the dedicated silicon dedicated to emerging application workloads in the phone. Instead we consider how in-memory processing using the DRAM already present in the phone could be used in place of dedicated GPU/TPU devices for AI inference. We explore the potential savings in embodied carbon that could be possible with this tradeoff and provide some analysis of the potential of in-memory computing to compete with these accelerators. While it may not be possible to achieve the same throughput, we suggest that the responsiveness to the user may be sufficient using in-memory computing, while both the embodied and operational carbon footprints could be improved. Our approach can save circa $10-15 \mathrm{~kg} \mathrm{CO}_{2}$.