Data Forwarding

Concept

Data forwarding is a microprocessor pipeline technique used alongside interlocking to preserve the sequential semantics of an instruction-set architecture (ISA) while overlapping instruction execution. In pipelined designs such as the Y86-64 PIPE, forwarding paths route values produced in later pipeline stages back to earlier stages where they are consumed, reducing or eliminating certain data-hazard stalls. In industrial designs such as the e500, data forwarding is one of several micro-architectural features used for early resolution of read-after-write (RAW) data dependencies.

First seen 5/25/2026

Last seen 8/5/2026

Evidence 8 chunks

Wiki v3

WIKI

Overview

Data forwarding is a microprocessor pipeline technique used alongside interlocking to make a pipelined implementation faithfully implement the sequential semantics of an instruction-set architecture (ISA). In the cited Y86-64 verification context, the ISA is described as a sequential model in which instructions execute in strict order and affect architectural state such as registers, the program counter, and memory. Pipelined implementations improve performance by overlapping multiple instructions, and forwarding/interlocking mechanisms help preserve the same architectural result as the sequential ISA model ^[1].

Role in handling hazards

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RELATIONSHIPS

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PIPE Pipeline Processor ← uses 100% 2e

PIPE uses data forwarding to resolve hazard conditions between instructions in the pipeline.

Pipeline Hazard ← mentions 100% 1e

Data forwarding is employed to handle pipeline hazards in pipelined processors.

MIPS Processor ← mentions 85% 1e

The MIPS processor was used to generate test programs for data forwarding in prior work

Dependency Chain Generation ← uses 90% 1e

Dependency chain generation is used to expose data forwarding hazards.

LiFU ← uses 85% 1e

LiFU is designed to expose data forwarding hazards in processor pipelines.

LINKED ENTITIES

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Dependency Chain Generation USES Extracted graph relationship

LiFU USES Extracted graph relationship

CITATIONS

8 sources

8 citations — click to expand

[1] In the Y86-64 PIPE, the signal m_valM computed in the memory stage feeds into the forwarding logic in the decode stage, creating cross-stage combinational dependencies that impose ordering constraints (writeback -> decode, memory -> execute -> decode) on the UCLID5 model. Formal Verification of Pipelined Y86-64 Microprocessors with UCLID5

[2] In the STALL pipeline variant, no data forwarding is used; in the LF variant, an added forwarding path connects the data-memory output to the pipeline register that feeds the data-memory input, allowing some load/use hazards to be resolved by forwarding rather than stalling. Formal Verification of Pipelined Y86-64 Microprocessors with UCLID5

[3] The PIPE design includes explicit forwarding-related decode-stage blocks (e.g., Sel+Fwd A and Fwd B) and signals such as W_valM, W_valE, and M_valA in its datapath and control logic. Formal Verification of Pipelined Y86-64 Microprocessors with UCLID5

[4] In the e500 processor (Power Architecture Technology), data forwarding is one of several micro-architectural features used for early resolution of RAW data dependencies, alongside multiple issue, out-of-order execution with in-order completion, register renaming, and reservation stations. Directed Micro-architectural Test Generation for an Industrial Processor: A Case Study

[5] Earlier pipeline-path-level model partitioning has been used to generate test programs that target data forwarding behavior, though it can cause memory deprivation during model checking. Directed Micro-architectural Test Generation for an Industrial Processor: A Case Study

[6] The directed micro-architectural test generation case study identifies four different data forwarding mechanisms as relevant configurations when validating forwarding logic in industrial processors such as the e500. Directed Micro-architectural Test Generation for an Industrial Processor: A Case Study

[7] Activating forwarding logic via fuzzing requires carefully ordered dependency-heavy instruction patterns (e.g., ADD -> STORE -> LOAD with the same address); longer multi-cycle forwarding hazards or dependent branches become exponentially harder to reach under random mutation, motivating learning-based and coordinated fuzzer ensembles. Beyond Fuzzer Islands: CPU Fuzzing via Smart Coordination

[8] Coordinated fuzzing ensembles can pipeline coordination stages in parallel with the slow execute stage to hide latency and process more testcases per unit time, an approach relevant to scaling coverage of microarchitectural features such as data forwarding. Beyond Fuzzer Islands: CPU Fuzzing via Smart Coordination