Zero-Knowledge Proofs Explained: Getting Started with SP1 zkVM Source Code

Zero-knowledge virtual machines (zkVMs) are revolutionizing how developers implement privacy-preserving and verifiable computation. Among the most promising in this space is SP1 zkVM, a RISC-V-based zero-knowledge virtual machine that allows developers to write programs in Rust and generate cryptographic proofs of correct execution—without needing deep expertise in low-level zk circuit design.

This guide dives into the architecture, core components, and source code structure of SP1, offering a practical walkthrough for developers interested in understanding how zkVMs work under the hood. Whether you're exploring scalable blockchain applications, decentralized identity, or verifiable AI, SP1 offers a powerful toolset worth mastering.

👉 Discover how SP1 enables secure, trustless computation using advanced zero-knowledge techniques.

Understanding SP1: A Modern zkVM Built on Plonky3

SP1 is a zero-knowledge virtual machine that supports the RISC-V instruction set architecture (ISA). It enables developers to compile standard Rust programs into executable ELF binaries, then generate succinct proofs that the program executed correctly—entirely within a zero-knowledge framework.

Unlike earlier approaches requiring manual circuit design using domain-specific languages (DSLs), SP1 abstracts away the complexity by leveraging Plonky3, a high-performance STARK prover. SP1 builds upon Plonky3’s Algebraic Intermediate Representation (AIR) to define constraints for a full CPU-like execution environment.

At its core, SP1 decomposes the virtual machine into modular components called chips, each responsible for specific aspects of computation:

• CPU operations
• Memory access
• Arithmetic logic
• Byte-level operations
• Program instruction handling

These chips communicate via structured interactions, ensuring correctness across subsystems while enabling efficient proof generation.

Core Architecture: How SP1 Uses AIR and LogUp

Algebraic Intermediate Representation (AIR)

SP1 relies on AIR (Algebraic Intermediate Representation) to express computational constraints as polynomial equations. Every operation in the VM—from instruction decoding to memory reads—is translated into constraints evaluated over a trace matrix.

Each chip implements the MachineAir trait, which extends Plonky3’s base Air interface with two key functions:

• eval: Defines polynomial constraints across execution trace rows.
• generate_trace: Produces the row-major execution trace used in proving.

For example, in a Fibonacci sequence computation (a common Plonky3 demo), constraints ensure:

builder.when_transition().assert_eq(local.right, next.left);
builder.when_transition().assert_eq(local.left + local.right, next.right);

This ensures each new value is the sum of the previous two—enforced mathematically through constraint evaluation over adjacent trace rows.

LogUp: Efficient Lookup Arguments for Chip Interactions

One of SP1’s innovations lies in its use of LogUp, a logarithmic derivative-based lookup argument. This technique verifies that values sent from one chip are correctly received and processed by another—without expensive equality checks.

LogUp proves that two sequences are permutations of each other by computing weighted sums using random challenges. If the total sum cancels out to zero, the sequences match up to reordering.

This approach powers:

• Memory consistency (proving every read corresponds to a prior write)
• Instruction fetch validation
• ALU and byte operation lookups

By converting correctness checks into permutation arguments, SP1 achieves both scalability and soundness.

👉 See how LogUp enhances proof efficiency in modern zkVMs like SP1.

Key Components of the SP1 Virtual Machine

CPU Chip

The CpuChip manages instruction execution flow. Its column layout (CpuCols) includes:

• Clock cycle (clk)
• Program counter (pc)
• Instruction opcodes
• Operand registers (op_a, op_b, op_c)

Constraints ensure:

• Correct incrementing of pc
• Valid instruction decoding
• Proper operand sourcing from register state

All operations are checked against expected behaviors defined in AIR.

Memory Chip

Memory access is modeled through two modes: Initialize and Finalize. The MemoryInitCols structure tracks:

• Access timestamp
• Memory address
• Data value (as a 32-bit word)
• is_real flag indicating actual usage

Critically, register file addresses are mapped into the same memory space starting at address 0, with register 0 permanently fixed to zero—mirroring RISC-V semantics.

Program Chip

The ProgramChip handles instruction loading and decoding. It separates data into:

• Preprocessed columns: PC and decoded instruction metadata
• Multiplicity columns: Runtime usage frequency during execution

This separation optimizes proof size by distinguishing static program content from dynamic execution patterns.

Byte Chip and ALU Operations

The ByteChip provides lookup tables for bitwise operations (AND, OR, XOR, shifts, etc.). With support for four parallel lookup channels, it enables concurrent processing of byte-level operations.

Similarly, the AddSubChip in the ALU module handles addition and subtraction, tracking operands and results with proper carry logic—all proven via constraint satisfaction.

Chip Interactions: Ensuring Cross-Component Consistency

A major challenge in zkVM design is verifying data flow between components. SP1 solves this using Interaction structures.

Each chip can:

• Send data it cannot verify locally (e.g., a memory read request)
• Receive data from other chips for validation (e.g., confirming a value was written before being read)

Interactions are categorized by type:

enum InteractionKind {
    Memory = 1,
    Program = 2,
    Alu = 4,
    Byte = 5,
    Range = 6,
    Syscall = 8,
}

Using LogUp, SP1 proves that all sent values are accounted for in received traces—ensuring global consistency without centralized coordination.

Execution Flow and Trace Generation

Programs execute within a Runtime environment that records every event:

• Instruction fetches
• Register updates
• Memory accesses
• System calls

These events are stored in an ExecutionRecord, later transformed into per-chip traces during proving.

Sharding splits long executions into manageable segments:

impl Default for SP1CoreOpts {
    fn default() -> Self {
        Self {
            shard_size: 1 << 22, // ~4 million cycles per shard
            ..
        }
    }
}

Each shard generates an individual proof, which can be recursively aggregated.

Recursive Proving and On-Chain Verification

To minimize verification costs, SP1 supports recursive proof composition. Multiple shard proofs are compressed into a single succinct proof using recursive SNARKs.

This final proof can be exported via Groth16, making it compatible with Ethereum and other EVM-based chains for lightweight on-chain validation—ideal for rollups, verifiable oracles, and private smart contracts.

Frequently Asked Questions (FAQ)

Q: What programming language does SP1 support?
A: SP1 supports Rust, allowing developers to write application logic in a familiar high-level language. The toolchain compiles Rust code into RISC-V ELF binaries for execution inside the zkVM.

Q: How does SP1 handle memory consistency?
A: Memory reads and writes are converted into permutation-equivalent sequences using LogUp. By proving these sequences are rearrangements of each other, SP1 ensures all reads return previously written values.

Q: Can SP1 proofs be verified on Ethereum?
A: Yes. While proofs are generated using STARKs (via Plonky3), they can be converted into Groth16 SNARKs for efficient on-chain verification—ideal for Layer 2 solutions and decentralized applications.

Q: What is sharding in SP1?
A: Sharding divides long program executions into smaller chunks (~4M cycles each). Each shard produces its own proof, improving parallelization and reducing memory pressure during proving.

Q: Is SP1 open source?
A: Yes. SP1 is fully open-source under the MIT license. The repository includes tooling, runtime, prover logic, and comprehensive examples to help developers get started quickly.

Q: How does SP1 compare to other zkVMs?
A: SP1 stands out for its RISC-V compatibility, modular chip architecture, and integration with Plonky3’s fast proving system. Its use of LogUp for cross-chip verification also improves efficiency compared to traditional lookup methods.

👉 Explore real-world use cases powered by SP1’s zkVM technology today.

Conclusion

SP1 represents a significant leap forward in accessible zero-knowledge proof systems. By combining a developer-friendly Rust-based workflow with rigorous formal verification through AIR and LogUp, it bridges the gap between complex cryptography and practical software engineering.

From decentralized finance to secure computation outsourcing, SP1 unlocks new possibilities for building transparent, verifiable, and privacy-preserving applications.

As zk infrastructure continues to evolve, tools like SP1 will play a foundational role in shaping the next generation of trustless systems.

Core Keywords:
zero-knowledge proofs, zkVM, SP1 zkVM, RISC-V, Plonky3, LogUp algorithm, AIR, Rust programming