Implementing spiking neural networks on neuromorphic architectures: A review
Recently, both industry and academia have proposed several different neuromorphic
systems to execute machine learning applications that are designed using Spiking Neural …
systems to execute machine learning applications that are designed using Spiking Neural …
A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications
Biological neural networks continue to inspire new developments in algorithms and
microelectronic hardware to solve challenging data processing and classification problems …
microelectronic hardware to solve challenging data processing and classification problems …
A survey of neuromorphic computing and neural networks in hardware
Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices,
and models that contrast the pervasive von Neumann computer architecture. This …
and models that contrast the pervasive von Neumann computer architecture. This …
Prime: A novel processing-in-memory architecture for neural network computation in reram-based main memory
Processing-in-memory (PIM) is a promising solution to address the" memory wall"
challenges for future computer systems. Prior proposed PIM architectures put additional …
challenges for future computer systems. Prior proposed PIM architectures put additional …
Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip
The new era of cognitive computing brings forth the grand challenge of developing systems
capable of processing massive amounts of noisy multisensory data. This type of intelligent …
capable of processing massive amounts of noisy multisensory data. This type of intelligent …
Backpropagation for energy-efficient neuromorphic computing
Solving real world problems with embedded neural networks requires both training
algorithms that achieve high performance and compatible hardware that runs in real time …
algorithms that achieve high performance and compatible hardware that runs in real time …
Drisa: A dram-based reconfigurable in-situ accelerator
Data movement between the processing units and the memory in traditional von Neumann
architecture is creating the" memory wall" problem. To bridge the gap, two approaches, the …
architecture is creating the" memory wall" problem. To bridge the gap, two approaches, the …
Minerva: Enabling low-power, highly-accurate deep neural network accelerators
The continued success of Deep Neural Networks (DNNs) in classification tasks has sparked
a trend of accelerating their execution with specialized hardware. While published designs …
a trend of accelerating their execution with specialized hardware. While published designs …
Cognitive computing building block: A versatile and efficient digital neuron model for neurosynaptic cores
Marching along the DARPA SyNAPSE roadmap, IBM unveils a trilogy of innovations towards
the TrueNorth cognitive computing system inspired by the brain's function and efficiency …
the TrueNorth cognitive computing system inspired by the brain's function and efficiency …
Cognitive computing programming paradigm: a corelet language for composing networks of neurosynaptic cores
Marching along the DARPA SyNAPSE roadmap, IBM unveils a trilogy of innovations towards
the TrueNorth cognitive computing system inspired by the brain's function and efficiency …
the TrueNorth cognitive computing system inspired by the brain's function and efficiency …