What’s New in the ASIC Chip Market?
By increasing the number of logic
gates per chip, the ASIC chip
technology reduced the size of electronic products. The market has time and
again witnessed the advent of various types and configurations of IC's. When we
look around, we note that some ICs can only be used for one particular
application, while some ICs can be reprogrammed and used for different
applications.
All you need to know about an ASIC chip
The abbreviation ASIC refers to
Application Specific Integrated Circuit. Such circuits are specific to the
applications, i.e., they are customized ICs for a specific application.
Usually, these are designed from the root level based on the specific
application's requirement. Some of the common application-specific integrated
circuit examples are chips used in toys, memory, and microprocessor interfacing
devices, etc. Such chips can only be used for the one application they are
designed for. Presumably, only those products that have a large production run
are chosen for these types of ICs. Because ASICs are engineered from the root
level, they have high costs and are only recommended for high volume
production.
Types of ASIC
·
Semi-Custom
Cells from standard libraries are
taken from this form of design logic. This means that they are not designed as
Full Custom Design. Some masks are personalized while the pre-designed library
takes some masks. These ASICs are classified into two categories, standard
cell-based ASIC and Gate Array-based ASIC, based on the type of logic cells
taken from the library and the amount of flexibility permitted for
interconnections.
·
Full
Custom
Unlike the semi-custom category
of ASICs, all logic cells are adapted to specific applications in this form of
design. The designer will make the logic cells for the circuits in particular.
All the interconnection mask layers are customizable. So the programmer can't
change the chip's interconnections and must be mindful of the circuit layout
when programming.
One of the full custom ASIC's
best examples is a microprocessor. This form of customization enables designers
to create on a single IC different analog circuits, customized memory cells, or
mechanical structures. This ASIC is expensive to manufacture and design and is
very cumbersome. It takes about eight weeks to develop these ICs.
ASICs are powering the future of IT today
·
Machine
learning:
Google's Tensor Processing Units
(TPU) are an ASIC type developed as part of the machine learning platform to
run key deep learning algorithms. Google initially used GPUs and CPUs to train
models for machine learning but has introduced a new generation of TPUs to
train and run the models. TensorFlow is the machine learning library developed
by Google that runs best on TPUs, as well as on both CPUs and GPUs.
·
Multi-cloud:
Enterprise IT, which allows
everything from social media to sporting events to ATMs, must be treated as a
multi-cloud environment holistically. Today, digital companies rely on a
combination of public cloud, private cloud, and on-premise hardware. ASICs can
sit in on-premise or a cloud environment as part of this process. ASICs are
already available via MLaaS in the multi-cloud, and this platform is already
being used by many organizations.
·
IoT
“edge” devices:
The circuitry baked into smart
devices is the driving force behind the digital revolution. IoT systems often
use custom-built ASICs to reduce the chip's physical space and work under low
energy requirements. In addition, IoT kits connect with cloud platforms such as
AWS IoT Core, TensorFlow, or Google Cloud–which may run ASICs internally. In
this way, IoT devices use ASICs to capture sensor data, move data into existing
algorithmic models running on cloud-based ASICs, and notify about other
outcomes back to the end-user from the model or simply feed the model to
accurately predict future outcomes.
ASICs and Artificial Intelligence
In order to support artificial
intelligence and related technologies, ASICs are now increasingly being
produced. One of the best examples is Google's own TPUs or Tensor Processing
Units, which are basically a series of machine learning ASICs designed for
running open-source machine learning applications. Many technology pioneers are
undertaking similar efforts, like Fujitsu's DLU or Deep Learning Unit. Google's
TPU is a good example of how to use an ASIC to solve very specific and narrow
functions and manage the workload in a parallel way.
To sum up
The ASIC chip
market is growing conjointly with revolutionary technologies. ASICs
drive digital transformation and are playing a pivotal role in data centers,
both private and public. The problem for today's experienced IT leaders is not
whether they should use ASICs (or FPGAs), but how best to combine this
technology with traditional CPUs and GPUs in the multi-cloud environment and
how best to manage costs through the software development and production
lifecycle implementation.
Taking risks like technical
obsolescence is often the responsibility of those companies that are
transforming the digital economy. Only well-funded projects that focus on
cutting-edge technology could be able to develop specialized ASIC chips–and
could be the only choice for digital leaders to stay at the forefront of their
markets.
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