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Whether powered by a battery or energy harvested from the environment, low-power (LP) sensor devices require extreme energy efficiency. These sorts of devices are becoming pervasive, running increasingly sophisticated applications in inhospitable environments. We present Manic, an energy-efficient microcontroller (MCU) augmented with a vector-dataflow (VDF) co-processor. The testchip taped out on a 22nm bulk finFET CMOS process demonstrates that Manic is 60% more energy-efficient than a baseline, scalar, low-power MCU, achieving peak efficiency of 256 MOPS/mW (2.6× prior work) while consuming only 19.1μW (@4MHz). To make the system viable for intermittently powered applications that require non-volatile storage, Manic includes a 256KB embedded MRAM. 

This paper presents the first 28 nm ASIC implementation of an accelerator for the post-quantum digital signature scheme XMSS. In particular, this paper presents an architecture for a novel, pipelined XMSS Leaf accelerator for accelerating the most compute-intensive step in the XMSS algorithm. This paper then presents the ASIC designs for both an existing non-pipelined accelerator architecture and the novel, pipelined XMSS Leaf accelerator. In addition, the performance of the28 nm ASIC is compared to the same designs on 28 nm Artix-7FPGAs. The novel pipelined XMSS Leaf accelerator is 25% faster compared to the non-pipelined version in the ASIC, and both accelerator architectures have a 10×lower power consumption than on the FPGAs. The evaluation shows that the pipelining increases the frequency by 1.7×on the FPGA but only 1.2×on the ASIC, due to the critical path in the ASIC being in the memory. The non-pipelined XMSS Leaf accelerator is shown to have a significantly better area-delay and energy-delay metric on the ASIC, while the pipelined accelerator wins out in these metrics on the FPGA. Consequently, this work shows the different architectural decisions that need to be made between FPGA and ASIC designs, when selecting how to best implement a post-quantum cryptographic accelerator in hardware.