Image credit: IBM Research / Flickr
GPUs have long been the standard hardware used for performing machine learning. To train today’s large deep learning models, practitioners and researchers often use GPUs, which require additional specialized knowledge to utilize for training models. The need for GPUs to train deep learning models has increased the barriers to performing research and creating products for those who do not have access to such specialized hardware. But Neural Magic, a startup created by MIT professor Nir Shavit, wants to change that. The startup, which recently released its first line of products, redesigned the deep learning software to run more efficiently on a CPU, achieving the same speed that a GPU would by avoiding the need to ferry data on and off the GPU. While Shavit thinks that CPUs will come to be “the actual fabric for running machine-learning algorithms,” MIT research scientist Neil Thompson is less sure, stating that while Neural Magic can squeeze more performance out of existing hardware, “fundamental hardware advancements will still be the only way to drive computing forward.”
NeurIPS, the world’s biggest and most recognizable AI conference, has made the news frequently. Recently, NeurIPS announced a requirement for a “societal impact” statement that would require authors to consider positive and negative impacts of their work. In another drastic shift, the organizers announced that the 2020 conference would be completely virtual and that tickets would be considerably cheaper. Indeed, NeurIPS’ shift towards inclusivity does signal its reckoning with the shortcomings of a less inclusive past. NeurIPS has reflected the field of AI’s demographics–mostly male and white–with the high cost of attendance and visa denials aiding homogeneity. In addition to the significantly reduced registration fee, a virtual conference will not require travel and lodging costs. Since NeurIPS has also removed the attendance cap, the conference will become more accessible to those who have not been able to attend in the past.
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