Beyond TCAM: Scale Packet Classification with Altera Stellar IP

PACKET CLASSIFICATION | FPGA | NETWORKING + SECURITY

Every packet decision starts with classification. Routers, firewalls, SmartNICs, security gateways, and service-edge platforms must determine which policy applies before traffic can be forwarded, blocked, inspected, redirected, or prioritized.

TCAM earned its place in these systems because it provides predictable lookup behavior and native ternary matching for ACL and LPM workloads. But as policy databases grow, search keys widen, and rules change more frequently, the traditional approach of scaling specialized ternary memory creates tougher tradeoffs in capacity, power, resource utilization, and system integration.

Altera Stellar IP takes a different approach. Instead of scaling packet classification by building ever-larger ternary-memory structures, Stellar turns classification into an optimized search problem that runs on configurable FPGA resources. The result is an FPGA-native path for large, dynamic packet-classification tables without giving up the policy semantics that made TCAM useful in the first place.

TCAM scales through memory expansion. Stellar scales through search intelligence. 

Keep the TCAM semantics. Change the scaling model.

Stellar preserves the intent of ACL, LPM, and multi-field classification, but changes how those rules are represented and searched. The Stellar Software Stack organizes policies into optimized graph structures, partitions the rule database, and continuously manages those search structures as the database evolves.

The hardware then executes the search using configurable FPGA search engines while rule information is stored in conventional memory resources. Depending on the configuration, Stellar can use on-chip M20K memory, eSRAM, DDR, or HBM. That gives architects more freedom to balance throughput, capacity, latency, power, and FPGA resource use around the needs of the actual system.

Use dense memory where it makes sense, and intelligence where it matters

A traditional TCAM combines storage and comparison circuitry inside specialized ternary-memory arrays. That architecture delivers deterministic matching, but scaling the table means scaling the specialized comparison structure as well.

Stellar separates rule storage from search execution. Ternary information can be represented as value-and-mask data in conventional memory, while graph organization and software-managed optimization direct each search toward the relevant parts of the database. As tables grow from thousands of entries toward hundreds of thousands or millions, this creates a different path for scaling capacity and power.

The white paper goes deeper into why this matters, including the tradeoffs between TCAM and Stellar, the role of memory hierarchy, and a set of Stellar configurations spanning different key widths, capacities, memory resources, and projected five-tuple performance.

Bring classification into the programmable datapath

The value is bigger than the lookup engine itself. Stellar is designed to operate inside the FPGA alongside packet parsing, telemetry, encryption, traffic management, host-interface logic, and customer packet-processing RTL.

For system architects, that means classification can become part of the same programmable platform already handling the datapath. A firewall can pair large, dynamic ACL processing with the rest of its traffic pipeline. A router can combine LPM and policy enforcement with programmable networking functions. A SmartNIC can integrate flow classification with offload and customer-specific acceleration.

That system-level flexibility is especially relevant for 100GE to 400GE designs where classification must scale without consuming the platform that surrounds it.

Read the full whitepaper!

The full Altera white paper, “Altera's TCAM Alternative Solution for Routers and Firewall Security Equipment,” explains the architecture behind Stellar and the design choices that make it different. It covers TCAM fundamentals and scaling limits, Stellar graph-based search and software partitioning, memory-hierarchy options, configuration examples, quantitative architectural comparisons, dynamic updates, and deployment use cases across networking and security.

Read the white paper

See how Stellar uses search intelligence, configurable FPGA resources, and a flexible memory hierarchy to create a scalable alternative for large ACL and LPM workloads.