Hardware-Cryptographic Co-Design Overview

Updated 30 November 2025

Hardware-cryptographic co-design is an approach that integrates cryptographic algorithms with hardware architectures to boost performance, security, and resource efficiency.
It employs iterative design loops where cryptographers and hardware architects jointly optimize parameters for parallelism, side-channel resistance, and cost-effective implementations.
This methodology enables resilient security across embedded systems, cloud computing, and post-quantum cryptosystems through detailed benchmarking and innovative case studies.

Hardware-cryptographic co-design denotes the rigorous, system-level integration of cryptographic algorithms and hardware architectural features, such that cryptographic security primitives and their dataflows are implemented directly in hardware (ASIC, FPGA, or custom architectures), with the design choices at each level informed by the requirements and constraints of the other. This paradigm leverages feedback loops between cryptographic specification and hardware implementation to achieve performance, side-channel resistance, flexibility, and resource efficiency unattainable through traditional hardware-then-software (or vice versa) sequential development. The co-design methodology is central to achieving resilient, high-assurance, and application-adaptive security for a spectrum spanning embedded systems, cloud computing, post-quantum cryptosystems, and privacy-preserving computation.

1. Methodological Foundations of Hardware-Cryptographic Co-Design

Hardware-cryptographic co-design is defined by tightly coupled iteration between:

Cryptographic primitive selection, parameterization, and protocol structure, subject to algorithmic hardness and security proofs.
Hardware mapping of those primitives with a focus on parallelism, resource sharing, and timing closure.

Typically, this is realized by one or more of the following methodologies:

Algorithmic decomposition into hardware-suitable kernels (e.g., polynomial multiply for Ring-LWE (Bu et al., 2019), S-boxes and MixColumns for AES (Lata et al., 2021), syndrome+majority decode for code-based cryptography (Bu et al., 2019), gate engines for garbled circuits (Mo et al., 2022)).
Co-design loops in which cryptographer and hardware architect jointly tune parameters ( $n$ , $q$ , error distributions, circuit sizes, etc.) to maximize both security and implementability (Bu et al., 2019, Mo et al., 2022).
Layered abstraction, where high-level IRs, ISAs, or block-diagram models enable hardware-software co-simulation and pipelined translation to HDL or hardware-verifiable code (Pan et al., 12 Sep 2025, Maheswaran et al., 20 May 2025, Saha et al., 2012).
Unified analytic frameworks, such as the Lightness Indicator System, for cross-domain performance and cost benchmarking (Damaj et al., 2019).

Co-design is often realized using toolchains that support rapid prototyping, system-level cosimulation, and automatic code generation—e.g., MATLAB/System Generator for FPGA-crypto vision (Saha et al., 2012), or high-level synthesis with domain-aware retiming for masked implementation (Sarma et al., 16 Jul 2024).

2. Hardware Architectures and Cryptographic Primitives

The hardware realization of cryptographic primitives spans a broad spectrum:

Stream and Block Ciphers: Pipelined and FSM-based AES/TDES on FPGAs, achieving Gbps throughput or minimal area via loop-unrolling vs. iterative round reuse (Lata et al., 2021, Ghosal et al., 2010).
Post-Quantum Primitives: Unified datapaths for Ring-LWE primitives, constant-latency PARM multipliers, and code-based McEliece variants with fully-parallel syndrome/majority-decode logic (Bu et al., 2019, Bu et al., 2019, Schöffel et al., 2023, Montanaro et al., 2022).
FHE and Pairing-Based Crypto: Modular NTT, Hadamard, and polynomial arithmetic engines (CoFHEE (Nabeel et al., 2022)); multi-level IR–ISA–hardware stacks for $\mathbb{F}_{p^k}$ algebra (Finesse (Pan et al., 12 Sep 2025)).
Privacy-Preserving Computation: Garbled-circuit engines with statically scheduled, instruction-streamed datapaths and banked scratchpads, as in HAAC (Mo et al., 2022).
Hardware Masking and Side-Channel Resistance: Domain-specific HLS flows that enforce register placement and glitch-resistant path balance for first- and second-order masking (Sarma et al., 16 Jul 2024).
Custom Microarchitecture Extensions: SHA-3/Keccak instruction pipelined into RISC-V cores, with full-vector register integration and cycle-level benchmarking (Bolat et al., 28 Aug 2025).

Hardware architectural patterns span full parallelism (e.g., $n^2$ multipliers in PARM (Bu et al., 2019)), deep pipelining, resource-multiplexed time-sharing (FSM approaches (Lata et al., 2021)), and banked/streamed scratchpads (HAAC SWW (Mo et al., 2022)). Reuse of building blocks across multiple primitives reduces area and maximizes utilization.

Architecture	LUT Usage (%)	FF Usage (%)	Max Throughput
AES-CTR FSM (Zed)	8,160 (8%)	6,420 (7%)	2.04 Gbps
TDES (Virtex-5)	1,690 (5%)	1,206 (4%)	296 Mbps
CV+RC4 (Spartan-3E)	3,200 (34%)	—	50 Mpixels/sec

3. Co-Design Patterns and Design-Space Exploration

Co-design mandates exploration of the trade-off space among area, throughput, latency, power, programmability, and security:

Parallelism vs. Area: Full unrolling, e.g., in polynomial-multiplier arrays, provides $O(1)$ latency at $O(n^2)$ area (Bu et al., 2019), while FSM architectures reuse limited logic at the cost of higher latency (Lata et al., 2021, Ghosal et al., 2010).
Resource Sharing: Unified datapaths for multiple primitives maximize utilization, at the risk of pipeline stalls or scheduling conflicts (Bu et al., 2019, Maheswaran et al., 20 May 2025).
Configurability and Modularity: Abstraction layers (IR, RISC-core, parameterized hardware) enable rapid retargeting to new curves, security levels, or future cryptosystems (Pan et al., 12 Sep 2025, Maheswaran et al., 20 May 2025).
Statistical Cross-Platform Benchmarking: Systems like LIS normalize, combine, and rank metrics from algorithmic, software, and hardware domains to inform optimal selector indices (Damaj et al., 2019).
Innovative Compilation and DSE: E-graph verified transformations (Cryptonite (Maheswaran et al., 20 May 2025)) and cycle-accurate simulation-driven schedule refinement (Finesse (Pan et al., 12 Sep 2025)) automate Pareto-optimal design exploration.

This exploration is, by necessity, application-dependent: high-assurance HSMs require threshold cryptographic protocols and physical diversity (Mavroudis et al., 2017), whereas IoT-grade PQC targets strict area/energy constraints at low-levels (Schöffel et al., 2023).

4. Security, Resilience, and Side-Channel Considerations

Hardware-cryptographic co-design directly addresses security concerns beyond functional correctness:

Backdoor and Fault Tolerance: Composition of threshold cryptography with redundant, FIPS-certified ICs achieves exponential increases in backdoor- and error-tolerance, as in the Myst HSM (Mavroudis et al., 2017).
Glitch-Resistant Masking: Retiming-based insertion of registers at precise locations blocks combinational glitches that can recombine shares, yielding close to theoretically minimal area and pipeline depth for security against power side-channel attacks (Sarma et al., 16 Jul 2024).
Side-Channel Hardening in High-Level Synthesis: Domain-specific rules in MaskedHLS preserve the invariants demanded by masking gadgets, avoiding the security failures common with generic HLS backends (Sarma et al., 16 Jul 2024).
Statistical Validation: Empirical side-channel leakage validation with TVLA and simulation confirms (or falsifies) adequately masked/co-designed implementations.

A plausible implication is that the intricate interaction between cryptographic structure and hardware timing, resource layout, and physical implementation mandates close co-design to preclude emergent vulnerabilities.

5. Case Studies in System-Level Integration

Key case studies exemplify the impact of co-design on distinct application domains:

Real-Time Image Crypto-Transmission: Combined MATLAB/System Generator and FPGA-based pipeline for image enhancement and stream-cipher transmission realizes sub- $\mu$ s latency for 5×5 filters and adaptive thresholding, integrating RC4 stream cipher for on-the-fly encryption (Saha et al., 2012).
Post-Quantum KEMs: HLS-based modular BIKE and HQC co-designs partition keygen, encapsulate/decapsulate, and decode routines between CPU and hardware accelerators, tailoring to SoC resource envelopes and achieving $1.3\times$ – $2.8\times$ speedups (Montanaro et al., 2022, Schöffel et al., 2023).
Homomorphic Encryption: ASIC implementation of fundamental polynomial arithmetic kernels supporting $n=2^{14}$ , 128-bit coefficient NTTs, delivers $2$–$3$ orders-of-magnitude improvement in power-delay product, with scalable multi-bank SRAM management (Nabeel et al., 2022).
Pairing Accelerators: Agile compile–simulate–iterate flows obtain $34\times$ speedup and $6.2\times$ improvement in area efficiency for BLS12-381/BN/optimal Ate pairing pipelines compared to FlexiPair and prior non-flexible ASICs (Pan et al., 12 Sep 2025).
Garbled Circuits: Stream-optimized, instruction-scheduled, and scratchpad-accelerated ASICs bridge the performance/efficiency gap, realizing speedups of $589\times$ (CPU baseline) and 2,627× (with HBM2), with energy down to $350\,\mathrm{pJ}$ per gate (Mo et al., 2022).
Domain-Aware High-Level Synthesis: Automated e-graph synthesis and latency-/area-directed exploration unlock up to $88.9\%$ area and $54.3\%$ latency reductions for ECC primitives (Fiat codebase) (Maheswaran et al., 20 May 2025).

6. Co-Design Limitations, Challenges, and Future Directions

Current hardware-cryptographic co-design approaches face several limitations:

Toolchain Security Awareness: Mainstream HLS flows lack semantics to guarantee side-channel security or masking unless domain-specific retiming and annotation capabilities are introduced (Sarma et al., 16 Jul 2024).
Resource Bottlenecks: Full unrolling strategies (e.g., $n^2$ multipliers) rapidly saturate available FPGA/ASIC resources, motivating hybrid (streaming/partial-unrolling) designs as a practical trade-off (Bu et al., 2019, Damaj, 2019).
Off-Chip Bandwidth: Memory and instruction throughput become system bottlenecks for large circuits; scheduling, streaming, and prefetch-aware compiler passes ameliorate but do not eliminate this (Mo et al., 2022).
Parameter Verification and Security Guarantees: Non-binary code-based cryptography, for example, demands careful analysis to avoid structural attacks when deviating from long-studied code families (Bu et al., 2019, Schöffel et al., 2023).
Quantum Co-Design: NISQ-era hardware simulation workflows (cuQuantum) ground asymptotic complexity claims in practical device limitations, but realization of large-scale cryptanalytic circuits remains distant (Harshvardhan et al., 2023).

Future work includes universal cryptographic IRs for cross-family primitives, automated formal verification of hardware-level side-channel security, further refinement of resource-optimal masking strategies, and continued unification of co-design flows with platform-aware benchmarking for heterogeneous embedded/cloud deployments.

In sum, hardware-cryptographic co-design operates at the critical interface between cryptographic hardness, physical resource constraints, and system-level security/performance requirements. Across a diversity of primitives, applications, and technology nodes, the co-design paradigm enables both principled, security-conscious innovation and practical, high-performance implementation, as substantiated in a growing body of arXiv research from circuit-level to fully integrated system deployments.