A new technical paper titled “Efficient Computation of Map-scale Continuous Mutual Information on Chip in Real Time” was published by researchers at MIT.
Find the technical paper here.
“In this paper, we introduce a new hardware accelerator architecture for MI computation that features a low-latency, energy-efficient MI compute core and an optimized memory subsystem that provides sufficient bandwidth to keep the cores fully utilized. The core employs interleaving to counter the recursive algorithm, and workload balancing and numerical approximations to reduce latency and energy consumption. We demonstrate this optimized architecture with a FieldProgrammable Gate Array (FPGA) implementation, which can compute MI for all cells in an entire 201-by-201 occupancy grid (e.g., representing a 20.1m-by-20.1m map at 0.1m resolution) in 1.55 ms while consuming 1.7 mJ of energy, thus finally rendering MI computation for the whole map real time and at a fraction of the energy cost of traditional compute platforms,” states the paper.
K. Gupta, P. Z. X. Li, S. Karaman, V. Sze, “Efficient Computation of Map-scale Continuous Mutual Information on Chip in Real Time,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2021.
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