Like I said earlier, the first reason is the number of supported ML platforms: Keras, MXNet, PyTorch, TensorFlow, CoreML, DarkNet. This solves the first problem — it increases the number of potential clients. The more diversified the tools that can be used by accelerator developer’s clients are, the more clients there can be.
The second reason is the TVM community. Currently, it includes more than 500 contributors, and the development is sponsored by such giants, as Amazon, ARM, AMD, Microsoft, Xilinx. Thanks to that, the chip developer automatically gets all the new optimizations and components of TVM, including the support of new frontend versions. At the same time, TVM is under the Apache license which allows one to use it in any commercial project while ensuring independence from big corporations. The developer of a new neural chip owns all IPs and decides himself what to share with the community.
What is more, the TVM ecosystem is constantly expanding. There are companies like OctoML, Imagination Technologies, and our company, for that matter. This allows the chipmakers to give their users a lot of various tools and services to maximize their devices performance which, in its turn, gives them an advantage over their competitors. For example, OctoML offers a service for improving neural networks performance. It is compatible with most ML frameworks and platforms: ARM (A class CPU/GPU, ARM M class microcontrollers), Google Cloud Platform, AWS, AMD, Azure, and others. Clients are getting the ability to automatically configure their models on target hardware which saves engineers time, which is usually wasted on manual optimization and performance testing. This gets them to market faster.
The third reason is actually more of a complex of reasons: the technical possibilities of TVM. Here is a list of the most important ones, according to our insights: