Artificial Intelligence Acceleration
Let’s speed up things!



If you see many algorithms which work on their own today, thank AI. AI helps coders provide a good user experience and, thus, a better technical solution with enhanced speed.
AI Accelerators are fully-performance machines precisely developed for effectively processing AI workloads. AI Accelerators help you boost your AI systems.
We at DTC design AI applications using these accelerators to speed up AI applications, including artificial neural networks, machine learning, robotics, and other data-intensive or sensor-data-driven tasks.
Accelerators for deep learning are hardware architectures created to enhance the performance, effectiveness, and precision of computers executing deep learning algorithms.
Types of Accelerators


Graphics Processing Unit (GPU)
GPUs are an excellent tool for expanding new data centers, which have evolved, accelerating AI code executions and modeling, thus speeding up the AI development process. GPUs are used everywhere, from Modern computers, AI, gaming, professional graphics, and many such things.

Vision Processing Unit (VPU)
Vision Processing Units are used by Robots, IoT, and Augmented and Virtual Reality Devices and accelerate integrated machine vision into all your smart devices.

Application-Specific Integrated Circuit (ASIC)
ASICs offer stronger IP protection because they are mainly designed for specific applications, and these integrated circuits are for a particular function rather than a general purpose.

Tensor Processing Unit (TPU)
TPUs are integrated circuits specifically designed for applications made by Google to enhance ML with many workloads.
Jet speed acceleration enablers of Bangalore, come to DTC!

Better Performance
Lessens the Latency
Scalability
Optimize Speed
Decisiveness
We know you'll have a few questions, so we have tried our best to answer a few here.

Most frequent questions and answers
AI accelerators are specialised processors that are meant to speed up these key ML activities, increase performance, and reduce the cost of implementing ML-based systems.
The requirement is to improve customer experience, employee productivity, and drive innovation.
- Graphics processing units (GPUs)
- Google’s Tensor Processing Unit (TPU)