  Spike-based encoders represent information as sequences of spikes or pulses,
which are transmitted between neurons. A prevailing consensus suggests that
spike-based approaches demonstrate exceptional capabilities in capturing the
temporal dynamics of neural activity and have the potential to provide
energy-efficient solutions for low-power applications. The Spiketrum encoder
efficiently compresses input data using spike trains or code sets (for
non-spiking applications) and is adaptable to both hardware and software
implementations, with lossless signal reconstruction capability. The paper
proposes and assesses Spiketrum's hardware, evaluating its output under varying
spike rates and its classification performance with popular spiking and
non-spiking classifiers, and also assessing the quality of information
compression and hardware resource utilization. The paper extensively benchmarks
both Spiketrum hardware and its software counterpart against state-of-the-art,
biologically-plausible encoders. The evaluations encompass benchmarking
criteria, including classification accuracy, training speed, and sparsity when
using encoder outputs in pattern recognition and classification with both
spiking and non-spiking classifiers. Additionally, they consider encoded output
entropy and hardware resource utilization and power consumption of the hardware
version of the encoders. Results demonstrate Spiketrum's superiority in most
benchmarking criteria, making it a promising choice for various applications.
It efficiently utilizes hardware resources with low power consumption,
achieving high classification accuracy. This work also emphasizes the potential
of encoders in spike-based processing to improve the efficiency and performance
of neural computing systems.
