What Is SRAM? Static Random Access Memory Explained for AI Chips.

What Is SRAM?

SRAM, short for Static Random Access Memory, is a type of semiconductor memory that stores information while electrical power remains available without requiring the periodic refresh operations used by DRAM.

SRAM is commonly used for processor caches, buffers, and other fast on-chip storage.

It is important in AI computing because processors repeatedly need quick access to small portions of data.

For example, a neural processing unit may use on-chip SRAM to hold intermediate values while performing neural network calculations.

Key Takeaways

• SRAM stores information without periodic refresh while power is maintained.
• It is commonly used for fast caches and local memory close to computing units.
• SRAM generally requires more silicon area per stored bit than DRAM, making large capacities comparatively expensive.

How Does SRAM Work?

A conventional SRAM cell stores a bit using a circuit that maintains one of two stable states while power is supplied.

A common design uses six transistors per memory cell.

The circuit retains its state without the repeated refresh process associated with typical DRAM cells.

When the processor needs information, suitable memory-control circuitry selects the relevant cells and reads their values.

SRAM can provide very fast access.

However, its circuit structure uses substantial transistor area relative to the amount of information stored.

This is one reason it is commonly used in smaller capacities near processing hardware.

SRAM vs. DRAM

Both SRAM and DRAM are volatile memory.

They lose stored information when power is removed.

SRAM: Uses circuits that maintain stored states without periodic refresh, commonly providing fast access at relatively high area cost.

DRAM: Stores information using capacitors that require periodic refresh, allowing much higher storage density.

For example, a CPU may contain several levels of SRAM-based cache.

The computer's main working memory may use DRAM.

The two technologies complement one another.

Why Is SRAM Used for CPU and GPU Caches?

Processors repeatedly access instructions and data.

Retrieving information from external memory can take more time and energy than accessing nearby on-chip storage.

SRAM caches hold selected information close to processing units.

For example, a GPU can use on-chip memory to reduce the need for repeated transfers from external DRAM or HBM.

The exact cache architecture varies by processor.

What Is an SRAM Scratchpad?

A scratchpad is a region of memory that software or hardware manages explicitly rather than relying entirely on automatic cache behavior.

Some AI accelerators use SRAM-based scratchpad memory to store data needed for upcoming calculations.

For example, a processor may load a block of matrix values into local SRAM.

It can then reuse those values across multiple calculations before retrieving additional data.

This can reduce memory traffic.

However, efficient use requires suitable data management and software design.

Why Does SRAM Matter for Physical AI?

Edge AI processors need efficient computing within limited energy and thermal budgets.

Moving information between a chip and external memory can consume power and time.

SRAM can help reduce these transfers when useful data fits locally.

However, adding more SRAM increases chip area and manufacturing cost.

Processor designers must balance local memory capacity against computing resources and other hardware requirements.

Frequently Asked Questions

Is SRAM the same as SSD storage?

No. SRAM is volatile working memory. SSDs provide persistent storage, commonly using NAND flash.

Does SRAM retain information when power is removed?

No. Conventional SRAM loses its information without power.

The Bottom Line

SRAM provides fast temporary storage close to computing hardware. Its role in caches and AI accelerators helps processors reuse data efficiently without repeatedly accessing slower memory.