As
traditional chip makers struggle to embrace the challenges presented by the
rapidly evolving AI software landscape, a San Jose startup has announced it has
working silicon and a whole new future-proof chip paradigm to address these
issues.
The SimpleMachines,
Inc. (SMI) team - which includes leading research scientists and
industry heavyweights formerly of Qualcomm, Intel and Sun Microsystems - has
created a first-of-its-kind easily programmable, high-performance chip that
will accelerate a wide variety of AI and machine-learning applications. The
chip, Mozart, is a TSMC 16-nanometer design that utilizes HBM2 memory and is
sampling as a standard PCIe card. Initial results have shown significant
speedups across a broad set of AI applications, unlike other specialized AI
chips. Examples include recommendation engines, speech and language processing,
and image detection, all of which can run simultaneously.
Mozart is
the result of 10 years of research at the University of Wisconsin-Madison that
included several high-profile papers and awards. The research was led by SMI's
Founder, CEO and CTO Karu Sankaralingam, also a computer science professor
at UW.
The
working chip demonstrates that a clean-slate design can deliver the performance
of custom-built single purpose processors while maintaining flexibility and
programmability. The chip's software interface includes direct TensorFlow
support as well as API's for C/C++ and Python. Future generations of the
chip will leverage its unique architecture to scale up and down the power
spectrum from enterprise-class high-performance systems to 5-watt IoT devices.
Mozart will be available via a PCIe card called Accelerando,
or via the Symphony Cloud Service (SMI's hosted cloud service with easy access
to public clouds like Azure, Google Cloud Platform, and AWS).
"Mozart
is an extremely complex chip, one of the few using HBM2 (high-bandwidth memory)
for this type of application," Sankaralingam said. "It took our silicon team under a week from having the chip in-house to
running applications on the device, putting us on the fast track to take our
first silicon (A0) to production."
Unlike many of the new chips
that have been designed for single workloads like image processing, Mozart's
strength is in its ability to adapt on the fly and accelerate a wide variety of
workloads across a diverse range of industries and applications, including
image detection, image classification, natural language processing, language
translation, network security, recommendation engines, graph processing, drug
discovery, and gene sequence alignment.
"The
chip's design can support very large models today and is capable of running up
to 64 different models simultaneously," said Greg Wright, SMI's Chief
Architect. "Our next-generation 7-nanometer design is expected to be ready to
sample by the end of 2021 and will be 20x faster on a diverse set of workloads
than current chips."
Mozart's architecture leverages
the concept of Composable Computing, which abstracts any software application
into a small number of defined behaviors. SMI's novel compiler integrates into
the backend of standard AI frameworks like TensorFlow to translate those
programs and reconfigures the hardware on the fly to result in a chip that
behaves as if it were originally designed for that application.
"SimpleMachines's solution is a radically new
software-centric approach that deploys a programmable platform with a
breakthrough software stack and compiler that enables the programmer to easily
optimize the hardware on the fly and get the performance of custom silicon with
a platform that supports hundreds of different use cases," Wright said.
SMI is initially targeting companies in the public
datacenter, network security, finance, and insurance industries for its Mozart
Platform with plans to disrupt the edge and mobile device markets in future
product generations. According to Allied Market Research, the global AI
chip market will reach $91 billion
by 2025, with growth rates of 45% a year until then. Market drivers include a
surge in demand for smart homes and smart cities, more investment in AI
startups, and the rise of smart robots.
"As fast, flexible computing becomes more accessible, AI will
be used by more industries for more applications more frequently, so chip design must evolve accordingly," Sankaralingam said. "We are disrupting the next wave of computing
with our breakthrough technology and are excited about the market opportunity,
especially for AI chips along the power spectrum."