Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: With the recent reincarnations of neuromorphic computing comes the promise of a new computing paradigm, with a focus on the design and fabrication of neuromorphic chips. A key challenge in design, hover, is that programming such chips is difficult.  proposes a systematic methodology with a set of tools to address this challenge. The proposed toolset is called NEUTRAMS (Neural network Transformation, Mapping and Simulation), and includes three key components: a neural network (NN) transformation algorithm, a configurable clock-driven simulator of neuromorphic chips and an optimized runtime tool that maps NNs onto the target hardware for better resource utilization. To address the challenges of hardware constraints on implementing NN models (such as the maximum fan-in/fan-out of a single neuron, limited precision, and various neuron models), the transformation algorithm divides an existing NN into a set of simple network units and retrains each unit iteratively, to transform the original one into its counterpart under such constraints. It can support both spiking neural networks (SNNs) and traditional artificial neural networks (ANNs), including convolutional neural networks (CNNs) and multilayer perceptrons (MLPs) and recurrent neural networks (RNNs). With the combination of these tools,  have explored the hardware/software co-design space of the correlation beten network error-rates and hardware constraints and consumptions. Doing so provides insights which can support the design of future neuromorphic architectures. The usefulness of such a toolset has been demonstrated with two different designs: a real Complementary Metal-Oxide-Semiconductor (CMOS) neuromorphic chip for both SNNs and ANNs and a processing-in-memory architecture design for ANNs.
['Yu Ji', 'Youhui Zhang', 'Shuangchen Li', 'Ping Chi', 'Cihang Jiang', 'Peng Qu', 'Yuan Xie', 'Wenguang Chen']
I am writing a research paper on a idea NEUTRAMS: Neural network transformation and co-design under neuromorphic hardware constraints
[{"from": "human", "value": "I am writing a research paper on a idea NEUTRAMS: Neural network transformation and co-design under neuromorphic hardware constraints"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: With the recent reincarnations of neuromorphic computing comes the promise of a new computing paradigm, with a focus on the design and fabrication of neuromorphic chips. A key challenge in design, hover, is that programming such chips is difficult.  proposes a systematic methodology with a set of tools to address this challenge. The proposed toolset is called NEUTRAMS (Neural network Transformation, Mapping and Simulation), and includes three key components: a neural network (NN) transformation algorithm, a configurable clock-driven simulator of neuromorphic chips and an optimized runtime tool that maps NNs onto the target hardware for better resource utilization. To address the challenges of hardware constraints on implementing NN models (such as the maximum fan-in/fan-out of a single neuron, limited precision, and various neuron models), the transformation algorithm divides an existing NN into a set of simple network units and retrains each unit iteratively, to transform the original one into its counterpart under such constraints. It can support both spiking neural networks (SNNs) and traditional artificial neural networks (ANNs), including convolutional neural networks (CNNs) and multilayer perceptrons (MLPs) and recurrent neural networks (RNNs). With the combination of these tools,  have explored the hardware/software co-design space of the correlation beten network error-rates and hardware constraints and consumptions. Doing so provides insights which can support the design of future neuromorphic architectures. The usefulness of such a toolset has been demonstrated with two different designs: a real Complementary Metal-Oxide-Semiconductor (CMOS) neuromorphic chip for both SNNs and ANNs and a processing-in-memory architecture design for ANNs."}]