Programmable Olfactory Computing

While 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 since a) a large number of driver applications are emerging, b) odor sensors are now dramatically better, and c) non-traditional form factors such as sensor, wearable, and xR devices that would be required to support olfactory computing are already getting 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, and 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 targeting extremely power and energy constrained olfactory computing workloads and evaluate it against baseline architectures of an MCU, a state-of-art CGRA, and an MCU with packed SIMD. Across our algorithms, Ahromaa's operating modes outperform the baseline architectures by 1.36, 1.22, and 1.1x in energy efficiency when operating at MEOP. We also show how careful design of data memory organization can lead to significant energy savings in olfactory computing, due to the limited amount of data memory many olfactory computing kernels require. These improvements to the data memory organization lead to additional 4.21, 4.37, and 2.85x improvements in energy efficiency on average.