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 …

Spiking Recurrent Neural Networks (SRNNs) present an alternative computing paradigm to
traditional von Neumann computing in the post-Moore's law era because they omit the …

Establishing reconfigurable hardware connectivity in a Spiking Recurrent Neural Network
(SRNN) is a challenging task due to the requirement of hardware capability for a fully …

Neuromorphic Computing presents a resource-efficient computing paradigm by enabling
brain-inspired low-power computations. This compute efficiency can be attributed in large …

The architectures of Network-on-Chip (NoC) are effective fabric for application specific
systems-on-chips (SoCs) and general purpose chip multi-processors (CMPs). The important …

The complexity and cost of system-on-chip (SoC) designs keep increasing every year, which
has progressively led to more opportunities for 2.5 D System-in-Package (SiP) design. While …

Neuromorphic computing mimics the functional components and structure of the human
brain to achieve highly efficient computing with minimal resources and power consumption …

In recent years, fast computation, low power, and scalability are the key motivations for
building SNN hardware. However, the unique features of SNN hardware have not been fully …

Buffer is a very crucial component in giving performance of an Network on Chip (NoC). In
short buffers governs the performance of an NoC. But the major problem with Buffers is that …