Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Deep neural networks are gaining in popularity as they are used to generate state-of-the-art results for a variety of computer vision and machine learning applications. At the same time, these networks have grown in depth and complexity in order to solve harder problems. Given the limitations in por budgets dedicated to these networks, the importance of low-por, low-memory solutions has been stressed in recent years. While a large number of dedicated hardware using different precisions has recently been proposed, there exists no comprehensive study of different bit precisions and arithmetic in both inputs and network parameters. In this work,  address this issue and perform a study of different bit-precisions in neural networks (from floating-point to fixed-point, pors of two, and binary). In our evaluation,  consider and analyze the effect of precision scaling on both network accuracy and hardware metrics including memory footprint, por and energy consumption, and design area.  also investigate training-time methodologies to compensate for the reduction in accuracy due to limited bit precision and demonstrate that in most cases, precision scaling can deliver significant benefits in design metrics at the cost of very modest decreases in network accuracy. In addition,  propose that a small portion of the benefits achieved when using lor precisions can be forfeited to increase the network size and therefore the accuracy.  evaluate our experiments, using three ll-recognized networks and datasets to show its generality.  investigate the trade-offs and highlight the benefits of using lor precisions in terms of energy and memory footprint.
['Soheil Hashemi', 'Nicholas Anthony', 'Hokchhay Tann', 'R. Iris Bahar', 'Sherief Reda']
I am writing a research paper on a idea Understanding the impact of precision quantization on the accuracy and energy of neural networks
[{"from": "human", "value": "I am writing a research paper on a idea Understanding the impact of precision quantization on the accuracy and energy of neural networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Deep neural networks are gaining in popularity as they are used to generate state-of-the-art results for a variety of computer vision and machine learning applications. At the same time, these networks have grown in depth and complexity in order to solve harder problems. Given the limitations in por budgets dedicated to these networks, the importance of low-por, low-memory solutions has been stressed in recent years. While a large number of dedicated hardware using different precisions has recently been proposed, there exists no comprehensive study of different bit precisions and arithmetic in both inputs and network parameters. In this work,  address this issue and perform a study of different bit-precisions in neural networks (from floating-point to fixed-point, pors of two, and binary). In our evaluation,  consider and analyze the effect of precision scaling on both network accuracy and hardware metrics including memory footprint, por and energy consumption, and design area.  also investigate training-time methodologies to compensate for the reduction in accuracy due to limited bit precision and demonstrate that in most cases, precision scaling can deliver significant benefits in design metrics at the cost of very modest decreases in network accuracy. In addition,  propose that a small portion of the benefits achieved when using lor precisions can be forfeited to increase the network size and therefore the accuracy.  evaluate our experiments, using three ll-recognized networks and datasets to show its generality.  investigate the trade-offs and highlight the benefits of using lor precisions in terms of energy and memory footprint."}]