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Sirius, renowned as the most luminous star in the night sky, deceives the naked eye by appearing as a solitary point of light when, in fact, it is a binary star system. Inspired by this duality, our project bears the name Sirius, capturing the essence of folded instances that give the illusion of being a singular entity.

Introduction

Sirius is an open-source Plonkish Folding Framework for Incrementally Verifiable Computation [IVC].

Within the context of an IVC scheme, the prover's role is to demonstrate that, upon consecutively applying a step function F exactly n times to an initial value $z_0$, the result is $z_n$. Here, the step function F takes two inputs $z_i$ and $w$, and yields an output $z_{i+1}$.

Architecture

The Sirius folding framework is designed with a three-tiered architecture.

Arithmetization Layer: This layer serves as the interface of the constraint system. User-defined circuits and witness data are converted into an intermediate representation format defined by the folding scheme. Our current implementation follows the special-sound interactive protocol (SPS) from Protostar.
Folding Scheme Layer: At the heart of the framework is the folding scheme IVC circuit that accumulates the computations of multiple steps. At each step, the prover first calculates the instance-witness pairs from the previous step and folds them into the accumulator, then computes the cross terms and error vector for the folded instance-witness pairs. An IVC circuit then takes the outputs from the prover and performs the following steps: apply the step function F, fold the previous step's instance into the accumulator instance, and verify the inputs of the IVC circuit.
SNARK Layer: The SNARK layer leverages Polynomial Interactive Oracle Proofs (PIOP) and Polynomial Commitment Schemes (PCS) to generate zkSNARKs for succinct and zero-knowledge verification. Polynomial relation checks of the IVC decider are converted to the multivariate sum-check protocol. The evaluation phase of the sum-check protocol depends on the polynomial commitment scheme (PCS) we choose, e.g. Hyrax. It is worth noting that when the polynomials are sparse, we can use the Spark compiler from Spartan to handle them efficiently.

Emerging Architectures for IVC

Sirius has evolved to support multiple IVC schemes that combine folding techniques with novel optimizations:

Sangria

Sangria adapts Nova's folding scheme for the more flexible PLONKish arithmetization. By incorporating relaxed gate equations with a scaling factor u and an error vector e, Sangria supports custom gates and higher-arity circuits while managing the additional cross-terms introduced by higher-degree constraints.

Cyclefold + Protogalaxy

Cyclefold represents a significant advancement in our folding scheme evolution. Its key insight is that folding-based recursive arguments can function without needing a full cycle of elliptic curves for every operation. Instead, Cyclefold delegates expensive non-native scalar multiplication and point addition operations to a compact "co-processor" circuit defined over a secondary elliptic curve. This dramatically reduces the size of the verifier circuit on the non-pairing-friendly curve and simplifies security reasoning.

While Cyclefold targets the efficiency of recursive proof composition, Protogalaxy NIFS focuses on optimizing high-degree gate constraints. In many PLONKish circuits, custom gates may have degrees higher than two—and naive folding would increase cryptographic work linearly with the degree. Protogalaxy NIFS introduces optimizations that reduce this overhead nearly to a constant cost per high-degree gate.

Together, these IVC schemes form the foundation of Sirius, offering different trade-offs between flexibility, efficiency, and security to meet diverse application needs.

Roadmap

2023Q4 - halo2 frontend support
2023Q4 - folding scheme for plonkish custom gates
2023Q4 - folding scheme for lookup arguments
2024Q1 - IVC circuit
2024Q1 - IVC Benchmarks
2024Q2 - high-degree gates optimization from Protogalaxy
2024Q3 - IVC with cyclefold

The estimated timeline is subject to change.

Getting Started

Install Rust

Use rustup

Add dependency

cargo add --git https://github.com/snarkify/sirius.git --tag v0.2.0 sirius

Implement `StepCircuit` trait

use sirius::ff::PrimeField;
use sirius::ivc::step_circuit::{ConstraintSystem, Layouter, AssignedCell, SynthesisError};

/// The StepCircuit trait is the foundation for creating circuits that can be folded.
/// It represents a single step of computation in an IVC chain.
///
/// `ARITY` - The number of input/output elements in your circuit
/// `F` - The field type used for circuit arithmetic
pub trait StepCircuit<const ARITY: usize, F: PrimeField> {
    /// Configuration type for your circuit gates and constraints
    type Config: Clone;
    
    /// Define the circuit's constraints and gates
    /// This is similar to halo2's configure method
    fn configure(cs: &mut ConstraintSystem<F>) -> Self::Config;
    
    /// This method implements the actual step function transformation: z_i → z_{i+1}
    ///
    /// Unlike standard `halo2::Circuit`, this method:
    /// - Takes an array of assigned input values (z_in)
    /// - Must return an array of assigned output values of the same size
    /// - Uses the same layouter pattern as halo2 for region assignments
    fn synthesize_step(
        &self,
        config: Self::Config,                // Circuit configuration from configure()
        layouter: &mut impl Layouter<F>,
        z_in: &[AssignedCell<F, F>; ARITY],  // Input values from previous step (or initial values)
    ) -> Result<[AssignedCell<F, F>; ARITY], SynthesisError>;
}

Setup and run `IVC` instance

For runnable examples, please check examples folder.

Sirius supports multiple IVC schemes: Sangria & Cyclefold

Run `Sangria IVC`

/// Example demonstrating how to use Sangria IVC for incrementally verifiable computation

use std::{array, num::NonZeroUsize};
use sirius::{
    commitment::CommitmentKey,
    ivc::{step_circuit::trivial, SangriaIVC},
    sangria_prelude::bn256::{new_default_pp, C1Affine, C1Scalar, C2Affine, C2Scalar},
    ff::Field,
};

/// Sangria uses a dual-circuit architecture:
/// 1. Primary circuit performs the actual computational steps
/// 2. Secondary circuit handles cryptographic operations for folding
const PRIMARY_ARITY: usize = 5;   // Number of state elements for main computation circuit
const SECONDARY_ARITY: usize = 1; // Number of state elements for helper circuit 

/// Configuration parameters - must be tuned based on circuit complexity
/// These are minimum values for the trivial example
const PRIMARY_COMMITMENT_KEY_SIZE: usize = 21;   // Controls polynomial degree (primary curve)
const SECONDARY_COMMITMENT_KEY_SIZE: usize = 21; // Controls polynomial degree (secondary curve)

const PRIMARY_CIRCUIT_TABLE_SIZE: u32 = 17;      // Minimum required table size for primary
const SECONDARY_CIRCUIT_TABLE_SIZE: u32 = 17;    // Minimum required table size for secondary

// Step 1: Initialize primary and secondary circuits
// Primary circuit operates on bn256 curve with 5 state elements
let primary_circuit = trivial::Circuit::<PRIMARY_ARITY, C1Scalar>::default();

// Secondary circuit operates on grumpkin curve with 1 state element
let secondary_circuit = trivial::Circuit::<SECONDARY_ARITY, C2Scalar>::default();

// Step 2: Set up commitment keys for polynomial commitments on both curves
let primary_key = CommitmentKey::<C1Affine>::setup(PRIMARY_COMMITMENT_KEY_SIZE, b"bn256");
let secondary_key = CommitmentKey::<C2Affine>::setup(SECONDARY_COMMITMENT_KEY_SIZE, b"grumpkin");

// Step 3: Create the public parameters that define the Sangria IVC instance
let public_params = new_default_pp::<PRIMARY_ARITY, _, SECONDARY_ARITY, _>(
    SECONDARY_CIRCUIT_TABLE_SIZE,
    &primary_key,
    &primary_circuit,
    PRIMARY_CIRCUIT_TABLE_SIZE,
    &secondary_key,
    &secondary_circuit,
);

// Step 4: Execute folding for 10 steps, starting with specified initial values
// This performs z_0 → z_1 → z_2 → ... → z_10 with proof accumulation
let result = SangriaIVC::fold_with_debug_mode(
    &public_params,
    &primary_circuit,
    array::from_fn(|i| C1Scalar::from(i as u64)),   // Primary initial state [0,1,2,3,4]
    &secondary_circuit,
    array::from_fn(|i| C2Scalar::from(i as u64)),   // Secondary initial state [0]
    NonZeroUsize::new(10).unwrap(),                 // Number of fold steps to perform
)
.unwrap();

For a complete working example with detailed comments about private inputs between steps, see the full implementation at examples/sangria_trivial.rs

Run `Cyclefold IVC`

/// Example demonstrating how to use Cyclefold IVC for incrementally verifiable computation
use std::array;

use sirius::{
    commitment::CommitmentKey,
    ivc::step_circuit::trivial,
    cyclefold_prelude::{
        bn256::{C1Affine, C1Scalar, C2Affine},
        PublicParams, IVC,
    },
    ff::Field,
};

const CIRCUIT_ARITY: usize = 5;   // Number of state elements in computation circuit

/// Configuration parameters - must be tuned based on circuit complexity
/// For production use, these values typically need to be larger
const PRIMARY_COMMITMENT_KEY_SIZE: usize = 23;   // Controls polynomial degree (primary curve)
const SECONDARY_COMMITMENT_KEY_SIZE: usize = 23; // Controls polynomial degree (secondary curve)
const PRIMARY_CIRCUIT_TABLE_SIZE: u32 = 20;      // Minimum required table size

// Step 1: Initialize the main computation circuit
// This circuit operates on the bn256 curve with 5 state elements
let circuit = trivial::Circuit::<CIRCUIT_ARITY, C1Scalar>::default();

// Step 2: Set up commitment keys for both curves
// Primary key for the main circuit (bn256 curve)
let primary_key = CommitmentKey::<C1Affine>::setup(PRIMARY_COMMITMENT_KEY_SIZE, b"bn256");
// Secondary key for the co-processor (grumpkin curve)
let secondary_key = CommitmentKey::<C2Affine>::setup(SECONDARY_COMMITMENT_KEY_SIZE, b"grumpkin");

// Step 3: Create public parameters for Cyclefold IVC
// Note: Parameters are mutable during initialization (unlike Sangria)
let mut public_params = PublicParams::new(
    &circuit,
    primary_key,
    secondary_key,
    PRIMARY_CIRCUIT_TABLE_SIZE,
)
.unwrap();

// Step 4: Initialize and execute IVC step-by-step with verification
// This demonstrates the incremental nature of Cyclefold IVC
let ivc_result = IVC::new(&mut public_params, &circuit, array::from_fn(|_| C1Scalar::ZERO)) // Initialize with z_0 = [0,0,0,0,0]
    .expect("Failed to initialize IVC (step=0)")
    .next(&public_params, &circuit)                                         // Compute z_1 = F(z_0, w_1)
    .expect("Failed to compute next step (step=1)")
    .verify(&public_params)                                                 // Verify the computation
    .expect("Failed to verify computation");

For a complete working example with detailed comments about private inputs between steps, see the full implementation at examples/cyclefold_trivial.rs

Key Differences Between Sangria and Cyclefold

Both examples demonstrate how to set up and run IVC instances, but they highlight important architectural differences:

Sangria IVC:

Uses a dual-circuit approach: primary for actual computation, secondary for cryptographic operations
Requires separate configuration of both circuits with matching commitment settings
Executes all steps at once via fold_with_debug_mode (i.e., runs 10 steps in a single call)
The public parameters are immutable

Cyclefold IVC:

Uses a single main circuit with a specialized co-processor architecture
The co-processor handles expensive elliptic curve operations internally
Follows a step-by-step execution model via new(), next(), and verify()
The public parameters are mutable during initialization
Typically offers better performance for recursive proof composition

Choose between them based on your specific requirements for efficiency, flexibility, and complexity.

Run examples

For runnable examples, please check examples folder.

# 're' is short for 'run example'

# Alias to run IVC with parameterization via cli arguments
cargo re-cli --help

# Alias for run the IVC for trivial `StepCircuit` (just returns its input unchanged)
cargo re-trivial

# Alias for run the IVC for the poseidon-circuit
cargo re-poseidon

Time-Profiling

Span lifetime tracking implemented, which allows you to understand in detail how long a particular step takes to complete.

# 're' is short for 'run example'

# By default, it will output all spans with a lifetime greater than 1s
cargo re-poseidon | python3 .scripts/build_profiling.py

# It is possible to specify the bottom border of the output span
cargo re-poseidon | python3 .scripts/build_profiling.py --min-runtime 0.1s

# You can also store logs and process them at a later date
cargo re-poseidon > log; cat log | python3 .scripts/build_profiling.py

Memory-Profiling

The dhat utility and the dhat-rs experimental crate are used

# Run dhat with default IVC (poseidon+trivial)
cargo cli-dhat

# Check available params of run
cargo cli-dhat --help

Benchmark

For benches, please check benches folder.

cargo bench

Criterion

You can also get a more detailed report. Please check for info criterion.rs

cargo criterion

Getting Involved

We'd love for you to be a part of our community!

If you're as enthusiastic about Sirius as we are, we invite you to join our developer community at Telegram. It's a great place to stay updated, get involved, and contribute to the project. Whether you're looking to contribute code, provide feedback, or simply stay in the loop, our Telegram group is the place to be.

👉 Join our developer community

Thank you for your interest in contributing to Sirius! ✨

License

snarkify/sirius

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