Edge-AI systems increasingly require real-time CNN inference under strict energy, performance, security, and privacy constraints. Approximate computing improves hardware efficiency by exploiting the error resilience of neural network workloads; however, most approximate CNN accelerators do not jointly consider secure, privacy-aware edge deployment. This paper presents SPARX, a Secure and Privacy-Aware Approximate CNN Acceleration framework integrated within a heterogeneous RV32IMC RISC-V System-on-Chip (SoC). SPARX combines a custom RISC-V instruction extension, an approximate logarithmic CNN acceleration unit, a lightweight differential-noise-based privacy engine, and a challenge-response authentication mechanism. To guide arithmetic selection, an approximation-aware decision framework is introduced that uses the Approximation Severity Index (ASI), Approximation Efficiency (AE), Quality of Approximation (QoA), Approximation Figure-of-Merit (AFOM), and Hardware Acceleration Efficiency (HAE). Evaluation across 11 state-of-the-art approximate MAC architectures identifies the Iterative Logarithmic Multiplier (ILM) as the most suitable design, achieving 51.7% area reduction, 81.5% power reduction, and 2.13x throughput improvement compared with an accurate radix-4 Booth MAC, while only reducing ResNet-20/CIFAR-10 accuracy by 2.82 percentage points. FPGA implementation on a Xilinx VC707 platform achieves 58.4 GOPS/W energy efficiency at 250 MHz, while 28-nm CMOS physical implementation validates ASIC feasibility

翻译：边缘AI系统日益需要在严格的能量、性能、安全性和隐私约束下实现实时CNN推理。近似计算通过利用神经网络工作负载的错误容限来提高硬件效率；然而，大多数近似CNN加速器并未联合考虑安全、隐私感知的边缘部署。本文提出SPARX，一种集成于异构RV32IMC RISC-V系统级芯片（SoC）中的安全隐私感知近似CNN加速框架。SPARX结合了自定义RISC-V指令扩展、近似对数CNN加速单元、轻量级差分噪声隐私引擎和挑战-响应认证机制。为引导算术选择，引入了一种近似感知决策框架，该框架使用近似严重性指数（ASI）、近似效率（AE）、近似质量（QoA）、近似品质因数（AFOM）和硬件加速效率（HAE）。对11种最先进近似MAC架构的评估确定迭代对数乘法器（ILM）为最合适的设计，与精确基4布斯MAC相比，面积减少51.7%，功耗降低81.5%，吞吐量提升2.13倍，同时仅使ResNet-20/CIFAR-10精度下降2.82个百分点。在Xilinx VC707平台上的FPGA实现于250 MHz频率下达到58.4 GOPS/W能效，而28纳米CMOS物理实现验证了ASIC可行性。