---
# System prepended metadata

title: 'Ponos Technology’s HW-Accelerated Plonky2 GoldiBear: Unleashing Plonky2 Over BabyBear Throughput'
tags: [cost, ZKP, Acceleration, HW, Recursion, low latency, high throughput, price, FPGA, Ponos Technology, Aggregation]

---

# Ponos Technology’s HW-Accelerated Plonky2 GoldiBear: Unleashing Plonky2 Over BabyBear Throughput

With the emergence of **recursive proving** and the convergence of **finite fields to smaller sizes**, Zero-Knowledge (ZK) proving is making significant strides in **reducing latency**, **increasing throughput**, and **cutting costs**. 

But why we would settle for less? 
By integrating **highly efficient hardware acceleration**, we can push these improvements even further. **Ponos Technology** once again showcases how our **cutting-edge FPGA designs** can take **ZKP** performance to **unprecedented heights**!

Telos R&D team has successfully adapted Polygon Plonky2 to the **BabyBear** field for the recursive circuit, unlocking faster and more efficient zero-knowledge proofs (ZKPs) aggregation. Building on that foundation, **Ponos Technology** has introduced **HW-accelerated Plonky2 GoldiBear**, a groundbreaking solution that merges hardware acceleration with optimized software to deliver unprecedented performance in **NTT** and **Poseidon2**-based Merkle tree computations.[[1](https://github.com/telosnetwork/plonky2_goldibear)]


## A Two-Pronged Innovation

1. **HW-Accelerated Plonky2 GoldiBear for the AMD Alveo U55C**
   Ponos has custom-built a pipeline on the AMD Alveo U55C FPGA, targeting BabyBear field operations for high-throughput NTT/INTT and parallelized Poseidon2 hashing. These specialized circuits operate in tandem with on-chip buffering to minimize latency and cut down polynomial processing times to a fraction of the CPU baseline.

2. **Zero-Overhead Software Memory Management**
   By reshaping the Plonky2 data-transfer routines, Ponos ensures polynomial coefficients reach the FPGA and inverse NTT results as well as Poseidon2 hashes reach the host with virtually no overhead. This fluid host-device data flow underpins the remarkable gains in efficiency.

## Benchmark Highlights

Measuring **polynomial-batch-intt-ext-ntt-merkelize** at batch sizes of **16**, **40**, and **334** polynomials illustrates the dominance of HW-accelerated Plonky2 GoldiBear:

| Batch Size | Software (ms) | FPGA (ms) | Speedup  |
|------------|---------------|-----------|----------|
| **16**     | ~12.9         | ~0.32     | ~40X     |
| **40**     | ~25.6         | ~0.90     | ~28X     |
| **334**    | ~190          | ~12.2     | ~15–16X  |

Across all sizes tested, **HW-accelerated Plonky2 GoldiBear** surpasses CPU performance by **15X to 40X**, underscoring its effectiveness and scalability.

## Unparalleled FPGA-Driven ZKP Acceleration

By tightly integrating **Telos’ Plonky2** (adapted to the BabyBear field) with **Ponos Technology’s** tailored FPGA acceleration, **HW-accelerated Plonky2 GoldiBear** positions itself as a powerful solution in the ZK proof aggregation landscape. Memory and pipeline optimizations ensure minimal transfer overhead, transforming once time-consuming polynomial and Merkle operations into swift computations.

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## Conclusion

The **HW-accelerated Plonky2 GoldiBear** from Ponos Technology isn’t just a high-speed outlier—it’s yet another hugely successful **HW-accelerated ZK prover backend**, standing alongside the company’s unmatched price-performance **Polygon Hermez zkEVM FPGA accelerator** and its **Aleo MSM FPGA accelerator** (featuring significant software prover backend parallelization).
