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In recent years, fast computation, low power, and small footprint are the key motivations for building SNN hardware. The unique features of SNN hardware have not been fully exploited, where the computation speed and energy efficiency of the SNN hardware can be improved according to the sparse spiking and non-uniform traffic of SNN. In this paper, we propose a 5.28-mm $^{2}~4096$ -neuron 1M-synapse energy-efficient digital SNN hardware that can achieve ultra-low energy per synaptic operation of 4.5 pJ. The proposed neuron computing unit is implemented in pipeline architecture to achieve high synaptic processing speed. The proposed spike processing unit can significantly increase the processing speed of the neuron core by $1.9\times $ and $9.4\times $ when the spike injection rate is 50% and 10%, respectively. Besides, the increase in the processing speed of the neuron core leads to a reduction in energy consumption of up to 81.5%. An event-driven clock gating circuit that can reduce the power consumption of the proposed neuron block by more than 70% is proposed in this paper. This paper proposes a supervised STDP+ algorithm for SNN training, and the classification accuracy of the MNIST digits is 89.6% with 73.6% weight sparsity of the output layer. [ABSTRACT FROM AUTHOR]

With the advent of technology over the recent years, the low-density parity-check (LDPC) codes, which were once seen as an impractical concept, are now poised to be the next big thing in the communication standards of today for their near-capacity performances. Nonetheless, the physical implementation of LDPC decoders is more often than not encumbered by the arithmetic of its decoding algorithm. Entangled by numerous computations of minima, LDPC decoders not only require considerable amount of resources to the implement cascaded pair-wise comparators, but also yield low decoding throughputs. In this paper, we propound a novel design for the computation of minimum and subminimum in LDPC decoding, known as the cognitive-amplification detector (CAD). By leveraging on the finite precision of fixed-point binary representation in actual hardware, our CAD proposition renders significant gains in decoding throughput and savings in resource consumption of up to 20% and 15%, respectively, not to mention negligible trade-offs in error-correcting capabilities. [ABSTRACT FROM AUTHOR]