Key takeaways
- The development of AI is prompting a shift towards brain-inspired architecture for improved computational efficiency.
- Computers using this new architecture can be significantly faster than traditional models, showcasing a renaissance in AI hardware.
- As computing requirements evolve, businesses are adopting tailored architectures for specific workloads, moving away from Von Neumann models.
- Advancements in neuroscience are driving the creation of cognitive systems that employ co-located memory and processing units.
- IBM is at the forefront, working on innovative architectures that enhance data processing for AI applications.
With the development of artificial intelligence intensifying across the globe, IT companies are looking for ways to revamp their architecture to make more robust. Increasingly, researchers are turning to brain-inspired architecture with co-located memory and processing, resulting in computers which are 200 times faster than conventional computers. Such is the excitement around AI hardware, that this phase has been dubbed as a “renaissance of hardware” as vendors are rushing to build domain-specific or workload-specific architectures that can significantly scale and improve computational efficiency.
And as we nudge forward in the mobile era, the workloads are going to look extremely dissimilar since the requirements of computing are changing. Businesses have to rely on a different architecture, each meant for a particular workload. This is where vendors are making a shift from Von Neumann computing architecture and are striving to improve the performance of computing with multi-core CPU architectures.
Now, the rapid gains in neuroscience have also spurred researchers across the globe to propose Brain-inspired computing architecture to develop highly advanced cognitive systems. IBM researchers are working on a new computer architecture which can process data efficiently for AI workloads. However, what’s remarkable is that this new architecture is inspired by the brain and will feature coexisting processing and memory units.
The IBM team argues that traditional computers were built on Von Neumann architecture, developed in the 1940s and featured a central processor that executes logic and arithmetic, a memory unit, storage, and input and output devices. But the current industry requirement has necessitated a move from homogeneous to heterogeneous computing architecture which has led to an increased research in applied materials and neuroscience.
Why Does AI Workload Require New Computing Architecture?
So what kind of changes are required in computing architecture for AI workload? According to researchers, there have to be consistent, ground-breaking breakthroughs in material sciences and neuroscience to advance AI processing. Let’s look at the two key requirements for AI workloads which have arisen an interest in brain-inspired computer architecture. Technologists emphasise that multi-core CPUs have reached their performance and efficiency limit and are adding to architectural challenges.- Memory requirement: Since AI workloads depend on a lot of data -- for processing huge amount of data, AI workloads require faster access to memory. In traditional CPUs have multi-level cache architecture which is not well-suited for AI.
- Parallel processing requirement: Parallel computing is on a rise and AI workloads and architectures have to be designed which can execute parallelism at scale.
