On-Chip Memory: Capacity 1-128 MB, Bandwidth 100-1000 GB/s

Component Specifications

Definition

On-chip memory refers to memory components fabricated directly on the same semiconductor die as the neural processing unit (NPU) processor cores. This integrated architecture provides ultra-low latency and high-bandwidth data access for neural network computations, eliminating the need for external memory interfaces during critical operations. It typically includes SRAM (Static Random-Access Memory) blocks organized in hierarchical structures (L1/L2 caches, scratchpad memory) optimized for tensor operations and weight storage in AI workloads.

Working Principle

On-chip memory operates by storing data in SRAM cells within the NPU die, using flip-flop circuits that maintain data as long as power is supplied. It employs memory controllers and interconnect fabrics to manage data movement between processor cores and memory banks. The working principle involves low-latency access through dedicated memory buses, cache coherence protocols for multi-core NPUs, and memory partitioning for simultaneous access to weights, activations, and intermediate results during neural network inference and training.

Materials

Silicon substrate with CMOS technology, copper interconnects, high-k metal gate transistors, silicon dioxide or advanced low-k dielectric insulation layers. Fabricated using semiconductor processes (e.g., 7nm, 5nm FinFET nodes) with multiple metal layers for routing.

Technical Parameters

Parameter	Typical range	Notes & selection driver
Latency	1-10 ns
Voltage	0.7-1.0V
Capacity	1-128 MB
Bandwidth	100-1000 GB/s
Technology Node	7nm-5nm
Operating Temperature	-40°C to 125°C

Ranges are indicative industry figures for RFQ preparation, not a supplier commitment. Confirm every value and standard with the legal manufacturer before ordering.

Standards

ISO 26262, JEDEC JESD209

Parent Products

This component is used in the following industrial products

Neural Processing Unit

A specialized hardware accelerator designed to efficiently process artificial neural network algorithms and machine learning workloads.

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Engineering Analysis

Risks & Mitigation

Thermal-induced data corruption
Process variation affecting yield
Soft errors from radiation
Voltage droop causing access failures

FMEA Triads

Trigger: Electromigration in interconnects
Failure: Increased resistance leading to timing violations
Mitigation: Use redundant vias and thicker metal layers

Trigger: Alpha particle strikes
Failure: Bit flips in SRAM cells (soft errors)
Mitigation: Implement error-correcting codes (ECC) and parity checking

Trigger: Power supply noise
Failure: Memory access failures during high-frequency operation
Mitigation: Design robust power delivery networks with decoupling capacitors

Industrial Ecosystem

Compatible With

Typical Suppliers & Equivalents

Compliance & Inspection

Tolerance

±5% for timing parameters, ±10% for voltage levels

Test Method

Automated test equipment (ATE) with memory BIST (Built-In Self-Test), shmoo plots for parametric testing, and functional verification with AI workloads

Procurement Evaluation Criteria

A practical evidence checklist for RFQ preparation and supplier evaluation.

Technical documentation

Request current drawings, revision history, and a signed specification sheet.

Manufacturing capability

Verify equipment lists, process limits, capacity, and representative production evidence.

Inspection readiness

Confirm test methods, calibrated equipment, sampling plans, and traceable reports.

Supplier transparency

Check the legal entity, factory address, ownership, certifications, and direct contacts.

CNFX does not score or rank suppliers. Buyers must verify all claims and documents with the legal manufacturer before ordering.

Manufacturers of On-Chip Memory

Manufacturer profiles associated with On-Chip Memory.

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Compiled and source-reviewed by the CNFX editorial team. Each manufacturer above links to the page on that company's own website where the product is listed, with the date we checked it. How we source and review

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Frequently Asked Questions

What is the difference between on-chip memory and off-chip memory in NPUs?

On-chip memory is integrated directly on the NPU die, offering nanosecond latency and terabyte-per-second bandwidth, while off-chip memory (like DDR or HBM) is external, with higher latency but larger capacity.

Why is on-chip memory critical for neural processing units?

It reduces data movement bottlenecks, enabling faster access to weights and activations, which improves energy efficiency and throughput in AI computations.

Can on-chip memory be upgraded or expanded?

No, on-chip memory capacity is fixed during manufacturing as part of the NPU's semiconductor design and cannot be modified after production.

Data Basis

Editorial classification, named public sources where available, and source-reviewed manufacturer records. See the editorial policy.

Preliminary Technical Classification

This page supports structured research, RFQ preparation, and supplier evaluation. It does not replace buyer-led supplier qualification, standards review, or technical approval.

On-Chip Memory