A Training Scheme for the Uncertain Neuromorphic Computing Chips
Uncertainty is a very important feature of the intelligence and helps the brain become a flexible, creative and powerful intelligent system. The crossbar-based neuromorphic computing chips, in which the computing is mainly performed by analog circuits, have the uncertainty and can be used to imitate the brain. However, most of the current deep neural networks have not taken the uncertainty of the neuromorphic computing chip into consideration. Therefore, their performances on the neuromorphic computing chips are not as good as on the original platforms (CPUs/GPUs). In this work, we proposed the uncertainty adaptation training scheme (UATS) that tells the uncertainty to the neural network in the training process. The experimental results show that the neural networks can achieve comparable inference performances on the uncertain neuromorphic computing chip compared to the results on the original platforms, and much better than the performances without this training scheme.
The paper is hard to read and there are syntactic errors as well as issues with the grammar. The paper is not at all well written and the contributions very questionable. The paper lacks a conclusion where the main contribution are mentioned and backup. Figures and illustrations are not the best. From my point of view, this paper is a clear rejection. I would encourage the authors to be explicit about their contribution and the intellectual products of this work. In addition, I would encourage them the identify comparable methods if any and explicitly enumerate the advantages of their approach against prior work/methods.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
A Training Scheme for the Uncertain Neuromorphic Computing Chips
Uncertainty is a very important feature of the intelligence and helps the brain become a flexible, creative and powerful intelligent system. The crossbar-based neuromorphic computing chips, in which the computing is mainly performed by analog circuits, have the uncertainty and can be used to imitate the brain. However, most of the current deep neural networks have not taken the uncertainty of the neuromorphic computing chip into consideration. Therefore, their performances on the neuromorphic computing chips are not as good as on the original platforms (CPUs/GPUs). In this work, we proposed the uncertainty adaptation training scheme (UATS) that tells the uncertainty to the neural network in the training process. The experimental results show that the neural networks can achieve comparable inference performances on the uncertain neuromorphic computing chip compared to the results on the original platforms, and much better than the performances without this training scheme.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The paper is hard to read and there are syntactic errors as well as issues with the grammar. The paper is not at all well written and the contributions very questionable. The paper lacks a conclusion where the main contribution are mentioned and backup. Figures and illustrations are not the best. From my point of view, this paper is a clear rejection. I would encourage the authors to be explicit about their contribution and the intellectual products of this work. In addition, I would encourage them the identify comparable methods if any and explicitly enumerate the advantages of their approach against prior work/methods.
assistant
The paper is hard to read and there are syntactic errors as well as issues with the grammar. The paper is not at all well written and the contributions very questionable. The paper lacks a conclusion where the main contribution are mentioned and backup. Figures and illustrations are not the best. From my point of view, this paper is a clear rejection. I would encourage the authors to be explicit about their contribution and the intellectual products of this work. In addition, I would encourage them the identify comparable methods if any and explicitly enumerate the advantages of their approach against prior work/methods.