An information-theoretic framework for joint architectural and circuit level optimization for olfactory recognition processing

Signal processing tasks such as classification or recognition may benefit from implementation strategies inspired by biological sensory pathways. In this paper, we employ an information-theoretic framework to explore the possible energy savings that can result from such an approach when applied to a specific problem, the artificial olfactory system. A preliminary evaluation of the efficiency versus SNR trade-offs for different signal representations demonstrates the advantage of one continuous-time discrete-valued (CTDV) signal representation in the low-precision regime with respect to the digital approach. These results are consequently applied to a joint circuit-architecture optimization of an artificial olfaction signal processing system, leading to promising indications of potential energy savings.