Given the task definition and input, reply with output. In this task, you are given a paper review. Based on the review, your job is to identify language and generate "en" if the review is in English or generate "es" if the review is in Spanish. Note that URLs in the text have been replaced with [Link].

This paper proposed a hardware accelerator for DNN. It utilized the fact that DNN are very tolerant to low precision inference and outperforms a state-of-the-art bit-parallel accelerator by 1.90x without any loss in accuracy while it is 1.17x more energy efficient. TRT requires no network retraining. It achieved super linear scales of performance with area. The first concern is that this paper doesn't seem very well-suited to ICLR. The circuit diagrams makes it more interesting for the hardware or circuit design community. The second concern is the "take-away for machine learning community", seeing from the response, the take-away is using low-precision to make inference cheaper. This is not novel enough. In last year's ICLR, there were at least 4 papers discussing using low precision to make DNN more efficient. These ideas have also been explored in the authors' previous papers.
en