SAN JOSE, Calif. — A Facebook executive confirmed reports that the social networking giant is hiring chip engineers and designing at least one ASIC. The news came at the @Scale event here, where Facebook announced that five chip companies will support Glow, an open-source, deep-learning compiler that it backs.

Facebook “is absolutely bringing up a silicon team focused on working with silicon providers, and we have a chip we’re building, but it’s not our primary focus,” said Jason Taylor, vice president of infrastructure at Facebook. The chip is “not the equivalent of [Google’s] TPU” deep-learning accelerator, he added, declining to provide further details on its focus or time frame.

Working with the estimated 50 companies designing AI accelerators is one focus for the new Facebook chip group. “There will be a lot of [accelerator] chips in the market,” said Taylor at a press roundtable. “The big question is whether the workloads they are designed for are the important ones at the time.”

In a keynote, Taylor described Glow as a generic compiler to let developers target any of the emerging deep-learning accelerators for inference in the cloud or at the edge of the network. It does not target client systems such as smartphones.

“We expect that there will be hardware fragmentation [in inference accelerators]. Our work with Glow is to help machine-learning experts design neural nets and not have to do the work required to tune them” to each unique chip.

“We know that the fragmentation is coming because no one knows what combination of [hardware] resources [such as on-chip memory blocks and multiply-accumulate arrays] will win, so we’ll let developers focus on the high-level graphs without hand-coding for the specifics of hardware.”

Jason Taylor described Glow as a compiler for inference on cloud and edge networks. (Images: Facebook)

Glow takes an AI graph produced by a framework such as TensorFlow or Caffe2 and renders it into byte code for hardware accelerators, explained Taylor. The compiler includes several tools including an instruction scheduler, a linear algebra optimizer, a memory allocator to generate efficient code for a chip’s specific memory configuration, and a CPU-based reference implementation for testing the accuracy of the hardware, according to a Facebook blog.

Cadence, Esperanto Technologies, Intel, Marvell, and Qualcomm said that they will support Glow on future chips. Taylor said that he expects to add others to the list. “That’s one of the benefits of it being open-source.”

One senior chip expert described Glow as a framework for deploying a neural network in production systems. Its input would be a graph created in a framework such as TensorFlow or Caffe2.

Some established chipmakers already supply similar software. For example, Nvidia’s Tensor RT takes in a graph from a framework and outputs Cuda code for its GPUs.

Traditionally, compilers are tightly optimized for a specific chip. But “what a compiler is these days is quite a bit broader than in the past — the kinds of optimizations in Glow have to do with identifying large portions of a graph that can be rendered to a hardware accelerator,” said Taylor.

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