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
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.
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.