Spiking Neural Networks (SNNs) have attracted considerable attention due to their suitability to processing temporal input streams, as well as the emergence of highly power-efficient neuromorphic hardware platforms. The computational cost of evaluating a Spiking Neural Network (SNN) is strongly correlated with the number of timesteps for which it is evaluated. To improve the computational efficiency of SNN evaluation, we propose layerwise disaggregated SNNs (LD-SNNs), wherein the number of timesteps is independently optimized for each layer of the network. In effect, LD-SNNs allowfor a better allocation of computational effort across layers in a network, resulting in an improved tradeoff between accuracy and efficiency. We propose a methodology to design optimized LD-SNNs from any given SNN. Across four benchmark networks, LD-SNNs achieve 1.67-3.84x reduction in synaptic updates and 1.2-2.56x reduction in neurons evaluated. These improvements translate to 1.25-3.45x faster inference on four different hardware platforms including two server-class platforms, a desktop platform and an edge SoC.