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We propose in this paper an efficient FALCON accelerator called EFX based on a HW/SW co-design where FALCON is a post-quantum cryptographic (PQC) scheme tailored as a digital signature algorithm (DSA). Our findings reveal that FALCON exhibits unique characteristics and structures which distinguish it from other PQC-DSAs. A key finding is that, unlike its counterparts, FALCON doesn’t prioritize a single, time-consuming task; instead, it processes a variety of tasks with comparable execution times. Consequently, the conventional methods focusing on accelerating dominant few tasks, which are generally effective for other algorithms, prove less efficient for FALCON, especially concerning the minimization of the silicon area used. To overcome this, we strategically focus on the granular optimization of lower-level operations rather than on broader functional segments, aiming to boost performance while conserving hardware space. Moreover, to mitigate the potential degradation due to limitation of hardware resources, we have implemented a pipelined execution strategy for the FALCON functions and refined the sampling function–a critical task that is challenging to accelerate due to inherent sequential algorithm–enabling it to run concurrently on both software and hardware, thus reducing latency. Our hardware design, synthesized at $300MHz$ using Samsung’s $28nm$ and $45nm$ process technologies, demonstrates superior performance in generating FALCON signatures, with a $3.58 \times $ improvement in clock cycles over an existing hardware accelerator. EFX occupies 38K $um ^{2}$ and 74K $um ^{2}$ for $28nm$ and $45nm$ processes, respectively, comparatively small compared to other PQC accelerators.

Remote computing has emerged as a trendy computing model that enables users to process an immense number of computations efficiently on the remote server where the necessary data and high-performance computing power are provisioned. Unfortunately, despite such an advantage, this computing model suffers from insider threats that are committed by adversarial administrators of remote servers who attempt to steal or corrupt users’ private data. These security threats are somewhat innate to remote computing in that there is no means to control administrators’ unlimited data access. In this paper, we present our novel hardware-centric solution, called MeetGo, to address the intrinsic threats to remote computing. MeetGo is a field-programmable gate array (FPGA)-based trusted execution environment (TEE) that aims to operate independently of the host system architecture. To exhibit the ability and effectiveness of MeetGo as a TEE ensuring secure remote computing, we have built two concrete applications: cryptocurrency wallet and GPGPU. MeetGo provides a trust anchor for these applications that enable their users to trade cryptocurrency or to run a GPGPU program server on a remote server while staying safe from threats by insiders. Our experimental results clearly demonstrate that MeetGo incurs only a negligible performance overhead to the applications.