Near-memory computing is an emerging hardware architecture that places processing capabilities closer to memory, reducing the distance data needs to travel and easing one of the biggest bottlenecks in modern computing. This article looks at how the approach works, why it matters for AI, and where its biggest challenges lie.
Modern computing systems are becoming increasingly dependent on fast data processing. From artificial intelligence and machine learning to high-performance computing and data analytics, processors are expected to handle enormous amounts of data in very short periods of time.
However, processor performance is not the only factor limiting modern computing. Data movement between processors and memory has become a major bottleneck.
Every time a processor needs data, that data typically has to travel between the processing unit and memory. This movement consumes time, bandwidth, and energy. As workloads continue to grow, moving data can become more expensive than processing the data itself — which is exactly the problem this architecture is designed to solve.
What Is Near-Memory Computing?
Near-memory computing is a computing architecture in which processing elements are placed physically close to memory rather than relying entirely on a separate processor located farther away.
In a traditional system, the architecture can be simplified as:
CPU → Memory → Data
The processor requests data from memory, processes it, and may then write the results back to memory.
With this approach, processing resources are positioned much closer to where the data is stored:
CPU → Near-Memory Processing → Data
The goal is not necessarily to eliminate the CPU or GPU. Instead, this kind of processing can handle certain operations closer to the memory subsystem while the main processor focuses on other computational tasks. The architecture becomes particularly valuable for workloads that repeatedly move large amounts of data.
Why Is Data Movement a Problem?
Traditional computer architectures were designed around the idea that processors would become significantly faster while memory systems would keep pace.
Over time, however, processor performance has increased rapidly while the latency and bandwidth characteristics of memory have created a growing gap — sometimes referred to as the memory wall.
A processor may be capable of performing billions or trillions of operations per second, but it still needs to wait when the required data is not available quickly enough.
Data movement also consumes energy. For workloads involving large datasets, a significant portion of system energy can be associated with transferring data between memory and processing units rather than performing the computation itself. This is the core problem the approach attempts to address by reducing unnecessary movement.
How Does Near-Memory Computing Work?
Near-memory architectures use specialized processing elements located close to memory devices. These elements can perform specific operations without sending every piece of data back to a distant CPU or GPU.
In a conventional architecture, processing a large dataset looks like this:
- Data is retrieved from memory.
- Data travels to the processor.
- The processor performs the calculation.
- Results are transferred back to memory.
- The process repeats for additional data.
With this technique, some calculations happen much closer to the memory, which can reduce:
- Data-transfer distance
- Memory traffic
- Latency
- Bandwidth pressure
- Energy consumption
The exact benefits depend on the workload and implementation.
Near-Memory Computing vs. In-Memory Computing
Near-memory computing and in-memory computing are closely related concepts, but they are not identical.
- Near-memory computing — processing elements are placed close to memory.
- In-memory computing — computation is performed within the memory itself, or using memory cells as part of the computational mechanism.
| Feature | Near-Memory Computing | In-Memory Computing |
|---|---|---|
| Processing location | Close to memory | Inside/within memory |
| Data movement | Reduced | Potentially minimized further |
| Architecture | Processing units + memory | Memory-centric processing |
| Implementation | Relatively flexible | More specialized |
| Main goal | Reduce data-transfer overhead | Compute where data resides |
Both approaches are being explored as alternatives to traditional processor-memory architectures. For additional technical background on the related concept, see IBM Research’s overview of in-memory computing.
Why Is Near-Memory Computing Important for AI?
Artificial intelligence is one of the areas where this architecture could have a significant impact — including at the edge, where AI hardware is increasingly handling inference locally.
Modern AI workloads often involve enormous matrices, neural-network parameters, and datasets. GPUs and AI accelerators perform large numbers of mathematical operations, but they also need to continuously move data between compute units and memory — creating substantial bandwidth and energy requirements.
Placing selected computation closer to high-bandwidth memory or other memory resources can help. For AI inference, this could potentially improve:
- Data-access latency
- Memory bandwidth utilization
- Energy efficiency
- Throughput
- Overall system efficiency
This is especially relevant as AI models become larger and more computationally demanding.
The Role of High-Bandwidth Memory
High-bandwidth memory (HBM) technologies have become increasingly important for AI accelerators and high-performance computing systems. HBM uses vertically stacked memory dies and wide interfaces to provide very high memory bandwidth.
This architecture can complement HBM by placing compute resources close to it. Instead of continuously transferring large volumes of data across a longer connection, selected operations can be performed closer to the memory subsystem. This combination could become increasingly important in future AI accelerator designs.
Benefits of Near-Memory Computing
1. Reduced Data Movement
The biggest advantage is reducing the amount of data that needs to travel between memory and a distant processor. Less movement can translate into lower latency and reduced bandwidth requirements.
2. Improved Energy Efficiency
Moving data consumes energy. By reducing unnecessary transfers, this architecture can potentially reduce the energy required for certain workloads — particularly important for large-scale AI systems and data centers.
3. Lower Memory Bottlenecks
When multiple processing units compete for access to memory, bandwidth can become a limiting factor. Near-memory processing can reduce pressure on the main memory subsystem by handling some operations locally.
4. Better Workload Scalability
As datasets become larger, conventional processor-memory architectures can struggle to maintain efficiency. Near-memory approaches offer another way to scale computing performance without relying exclusively on faster central processors.
5. Potential Performance Improvements
Reducing data movement can improve application performance, particularly for workloads that are heavily dependent on memory bandwidth.
Challenges of Near-Memory Computing
Despite its potential, this architecture also introduces several challenges.
Hardware Complexity
Adding processing capabilities closer to memory can make hardware architectures more complex. Designers must carefully balance compute resources, memory capacity, bandwidth, thermal constraints, and physical space.
Programming Challenges
Software needs to understand where computation should happen. Developers may need new programming models, compilers, APIs, or runtime systems to efficiently take advantage of near-memory processing.
Thermal Management
Putting additional processing resources close to memory can increase local heat generation. Thermal design becomes particularly important in high-performance systems where memory and compute components operate at high utilization.
Cost
Specialized memory architectures and advanced packaging technologies can increase manufacturing complexity and system cost.
Limited Workload Compatibility
Not every workload benefits equally from this approach. Applications with high data movement and relatively simple operations are generally more attractive candidates than workloads that are primarily compute-bound.
Near-Memory Computing and Advanced Packaging
Advanced semiconductor packaging is another technology that can help enable this class of architecture. Technologies such as 2.5D and 3D packaging allow processors, accelerators, memory, and other components to be positioned much closer together.
This physical proximity can enable shorter interconnects and higher bandwidth communication. As chip designs become increasingly heterogeneous, packaging is becoming an important part of system architecture rather than simply a final manufacturing step.
This works especially well alongside high-bandwidth memory — for a deeper look at how the latest memory technology is supporting AI workloads, see our guide on HBM4 and the memory tech powering the next AI boom.
The Future of Near-Memory Computing
The demand for computing continues to grow, particularly in AI, cloud computing, scientific research, and data analytics. Simply building faster processors may not be enough to address future performance requirements.
The industry is increasingly exploring architectures that improve the entire path between computation and data, and this is one of those approaches.
Future systems may combine:
- CPUs
- GPUs
- AI accelerators
- High-bandwidth memory
- Near-memory processing
- Advanced packaging
- High-speed interconnects
Instead of relying on a single processing architecture, these systems can distribute computation according to where it can be performed most efficiently.
Conclusion
Near-memory computing represents a shift toward bringing computation closer to data. As modern workloads become increasingly data-intensive, the cost of moving data between processors and memory is becoming a major architectural challenge.
By placing processing capabilities closer to memory, near-memory computing can potentially reduce data movement, improve bandwidth utilization, lower latency, and increase energy efficiency. The technology is particularly promising for AI and high-performance computing, where massive datasets and memory-intensive operations are becoming standard.
As advanced packaging, high-bandwidth memory, and specialized accelerators continue to evolve, near-memory computing could become an important building block for the next generation of computing systems.
