  Chiplets have become a common methodology in modern chip design. Chiplets
improve yield and enable heterogeneity at the level of cores, memory subsystem
and the interconnect. Convolutional Neural Networks (CNNs) have high
computational, bandwidth and memory capacity requirements owing to the
increasingly large amount of weights. Thus to exploit chiplet-based
architectures, CNNs must be optimized in terms of scheduling and workload
distribution among computing resources. We propose Shisha, an online approach
to generate and schedule parallel CNN pipelines on chiplet architectures.
Shisha targets heterogeneity in compute performance and memory bandwidth and
tunes the pipeline schedule through a fast online exploration technique. We
compare Shisha with Simulated Annealing, Hill Climbing and Pipe-Search. On
average, the convergence time is improved by ~35x in Shisha compared to other
exploration algorithms. Despite the quick exploration, Shisha's solution is
often better than that of other heuristic exploration algorithms.
