Zero-Knowledge Proofs (ZKPs) have emerged as a powerful tool for secure and privacy-preserving computation. ZKPs enable one party to convince another of a statement's validity without revealing anything else. This capability has profound implications in many domains, including: machine learning, blockchain, image authentication, and electronic voting. Despite their potential, ZKPs have seen limited deployment because of their exceptionally high computational overhead, which manifests primarily during proof generation. To mitigate these overheads, a (growing) body of researchers have proposed hardware accelerators and GPU implementations of kernels and complete protocols. Prior art spans a wide variety of ZKP schemes that vary significantly in computational overhead, proof size, verifier cost, protocol setup, and trust. The latest, and widely used ZKP protocols are intentionally designed to balance these trade-offs. A particular challenge in modern ZKP systems is supporting complex, high-degree gates using the SumCheck protocol. We address this challenge with a novel programmable accelerator to efficiently handle arbitrary custom gates via SumCheck. Our accelerator achieves upwards of$1000 \times$geomean speedup over CPU-based SumChecks across a range of gate types. We include this unit in zkPHIRE, a programmable, full-system accelerator that accelerates the HyperPlonk protocol. zkPHIRE achieves$1486 \times$geomean speedup over CPU and$11.87 \times$geomean speedup over the state-of-the-art at iso-area. Together, these results demonstrate compelling performance while scaling to large problem sizes (upwards of 230constraints) and maintaining small proof sizes ($4-5$KB).

Zero-Knowledge Proofs (ZKPs) are a rapidly growing technique for privacy-preserving and verifiable computation.ZKPs enable one party (a prover: P) to prove to another (a verifier: V) that a statement is true or correct without revealing any additional information.This powerful capability has led to ZKPs being applied and proposed for application in blockchain technologies, verifiable machine learning, and electronic voting.However, ZKPs have yet to see widespread, ubiquitous adoption due to the exceptionally high computational complexity of the proving process.Naturally, there has been recent work to accelerate ZKP primitives and protocols using GPUs and ASICs.However, the protocols considered so far face one of two challenges: they require a trusted setup for each new application or generate large proofs with high verification costs, limiting their applicability in scenarios with numerous verifiers or strict verification time constraints.HyperPlonk is a state-of-theart ZKP protocol that supports both one-time, universal setup and small proof sizes/verification costs expected by publicly verifiable, consensus-based systems (e.g., blockchain).While HyperPlonk's setup and verifier properties are highly desirable, the proving phase is costly.A HyperPlonk prover must compute on large bitwidths (e.g., 255-381b) and polynomials (e.g., of degree 2 24 ), employs computationally (e.g., MSM) and bandwidth (e.g., SumCheck) intensive kernels, and the complete protocol comprises many steps, each constituting distinct kernels.We present an accelerator, zkSpeed, to

Zero-Knowledge Proofs (ZKPs) are an emergent paradigm in verifiable computing. In the context of applications like cloud computing, ZKPs can be used by a client (called the verifier) to verify the service provider (called the prover) is in fact performing the correct computation based on a public input. A recently prominent variant of ZKPs is zkSNARKs, generating succinct proofs that can be rapidly verified by the end user. However, proof generation itself is very time consuming per transaction. Two key primitives in proof generation are the Number Theoretic Transform (NTT) and Multi-scalar Multiplication (MSM). These primitives are prime candidates for hardware acceleration, and prior works have looked at GPU implementations and custom RTL. However, both algorithms involve complex dataflow patterns – standard NTTs have irregular memory accesses for butterfly computations from stage to stage, and MSMs using Pippenger’s algorithm have data-dependent memory accesses for partial sum calculations. We present SZKP, a scalable accelerator framework that is the first ASIC to accelerate an entire proof on-chip by leveraging structured dataflows for both NTTs and MSMs. SZKP achieves conservative full-proof speedups of over 400 ×, 3 ×, and 12 × over CPU, ASIC, and GPU implementations.

RF system development is traditionally constrained by a restrictive trade-off between power efficiency and programmatic flexibility. We outline a path towards achieving both, thereby enabling a range of new system concepts that better utilize limited resources. As an example, for many future applications, we consider RF convergence – reusing the same spectrum and waveforms to achieve multiple distributed system functions and goals, simultaneously. To enable this next step in processing, we develop a novel framework that includes both software and the system-on-chip (SoC) design.