  This paper proposes a two-step spike encoding scheme, which consists of the
source encoding and the process encoding for a high energy-efficient
spiking-neural-network (SNN) acceleration. The eigen-train generation and its
superposition generate spike trains which show high accuracy with low spike
ratio. Sparsity boosting (SB) and spike generation skipping (SGS) reduce the
amount of operations for SNN. Time shrinking multi-level encoding (TS-MLE)
compresses the number of spikes in a train along time axis, and spike-level
clock skipping (SLCS) decreases the processing time. Eigen-train generation
achieves 90.3% accuracy, the same accuracy of CNN, under the condition of 4.18%
spike ratio for CIFAR-10 classification. SB reduces spike ratio by 0.49x with
only 0.1% accuracy loss, and the SGS reduces the spike ratio by 20.9% with 0.5%
accuracy loss. TS-MLE and SLCS increases the throughput of SNN by 2.8x while
decreasing the hardware resource for spike generator by 75% compared with
previous generators.
