Programmable Olfactory Computing

Although smell is arguably the most visceral of senses, olfactory computing has been barely explored in the mainstream. We argue that this is a good time to explore olfactory computing as driver applications are emerging, sensors are dramatically better, and nontraditional form factors that would be required to support olfactory computing have widespread acceptance. Through a comprehensive review of literature, we identify the key algorithms needed to support a wide variety of olfactory computing tasks. We profiled these algorithms on existing hardware and identified several characteristics, including the preponderance of fixed-point computation, linear operations, and real arithmetic; a variety of data-memory requirements; and opportunities for data-level parallelism. We propose Ahromaa, a heterogeneous architecture for olfactory computing that targets power- and energy-constrained olfactory computing workloads, and evaluate it against the baseline architectures of a microcontroller unit (MCU), coarse-grained reconfigurable array, and an MCU with packed single instruction, multiple data.