EDGE COMPUTING & AI
(typically cloud) server offers many potential benefits such as
removing latency in processing and reducing data transmission
and bandwidth, and it may also increase privacy and security.
In light of these advantages, the growth of the edge AI chips
market has been remarkable - the first commercially available
enterprise edge AI chip only launched in 2017, yet Deloitte
predicts that more than 750 million edge AI chips will be sold in
2020.
The global AI chip market as a whole was valued at $6.64bn
in 2018, and is projected to grow substantially in upcoming
years, to reach $91.19bn by 2025, increasing at compound
annual growth rate of 45.2%. Understandably, a wide range of
companies are, therefore, working to develop AI chips. However,
the market is poised to go through a growth cycle similar to
those seen in the CPU, GPU and baseband processor markets,
ultimately maturing to be dominated by a few large players.
IP, and patents in particular, have been key to the success of
household names such as Intel, Qualcomm and ARM, and it will
likely play a similarly prominent role in the AI chip arena.
The range of companies competing in the AI chips market
spans from ‘chip giants’ such as Intel, Qualcomm, ARM or
Nvidia, through to traditionally internet-focused tech companies
(e.g. Alphabet or Baidu) and numerous niche entities including
Graphcore, Mythic, or Wave Computing. Various large corporations
that would normally seem like ‘outsiders’ in a chip market
are also involved – for example, since the vast majority of
edge AI chips (90%) currently go into consumer devices, many
smartphone manufacturers have not missed this opportunity
and developed their own AI accelerators (e.g. Apple’s eight-core
Neural Engine used in its iPhone range).
The race currently remains open as to who may dominate.
Both technical specialists and investors will be looking closely
at which companies’ technology shows most promise, and the
field will inevitably evolve through investment, acquisitions and
failures. Within the next few years, we can expect to see the
market leaders emerge. Who will become to AI chips what Intel
has become to CPUs (77% market share), and what Qualcomm
is to baseband processors (43% market share)?
The current frontrunners appear to be Intel and Nvidia. According
to Reuters, Intel’s processors currently dominate the
market for AI inference, while Nvidia dominates the AI training
chip market. Neither Intel nor Nvidia are resting on their laurels,
as showcased by their recent acquisitions and product releases
that seem to be aimed at ‘dethroning’ one another. Just in
December 2019, Intel acquired Habana Labs, an Israel-based
developer of deep learning accelerators, for $2bn. Habana’s
Goya and Gaudi accelerators include a number of technical
innovations such as support for Remote Direct Memory Access
(RDMA) – direct memory access from one computer’s memory
to that of another without using either computer’s operating
system – a feature particularly useful for massively parallel
computer clusters and thus for the training of complex models
on the cloud (where Nvidia currently dominates). Nvidia, on the
other hand, recently released its Jetson Xavier NX edge AI chip
with an impressive up to 21 TOPS of accelerated computing,
directed in particular at AI inference.
Several smaller entities also look exciting, such as Bristolbased
Graphcore, or Mythic based in the US. Graphcore
recently partnered up with Microsoft, raising $150mat a $1.95bn
valuation. Their flagship product – the Intelligence Processing
Unit (IPU) - has impressive performance metrics and an interesting
architecture – e.g. the IPU holds the entire ML model inside
the processor using In-Processor Memory to minimise latency
and maximise memory bandwidth. Mythic’s architecture is
equally noteworthy, and combines hardware technologies such
as computing-in-memory (removing the need to build a cache
hierarchy), a dataflow architecture (particularly useful for graphbased
applications such as inference), and analog computing
(computing neural network matrix operations directly inside the
memory itself by using the memory elements as tunable resistors).
Mythic is not lagging behind Graphcore in commercial
aspects either – it added $30m in funding in June 2019 from
household investors such as Softbank.
Qualcomm and Intel patent families.
At this stage it is unclear who will eventually dominate the AI
chips market, but a key lesson from historical developments,
such as in the fields of CPUs and baseband processors, is
that IP rights play a big role in who comes out on top, and who
survives in the long run.
The importance of a strong patent portfolio for commercial
success in chips markets is demonstrated by the number of
patent filings of companies such as Intel or Qualcomm. These
have been increasing since 1996 and now sit at about 10,000
published patent families per year.
www.eenewseurope.com eeNews Europe April 2020 News 41
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