SYNAPSE - A New Approach to Semi-automated Design of Ultra-low-power Application-specific Embedded Processors

X. Ji, T. Kazmierski, B. Halak (Univ. of Southampton, UK)

The main contribution of this paper is a new opproach to semi-automated synthesis of Application Specific Embedded Processors (ASEPs). Designers of ASEPs do not have the comfort of standardized software support because the instruction sets are customised. Therefore, ASEP designs are frequently performed manually. In recent years a growing interest in ASEPs has been observed which can be explained by the following two factors. Firstly, in contrast to general-purpose processors, ASEPs are particularly resilient to cybersecurity threats, which nowadays affect both software and hardware in modern SoC applications, especially in critical areas such as medicine, communication or smart grids. Secondly, the usual justification for using ASEP designs is their excellent performance and power efficiency characteristics which are comparable or can exceed that of dedicated hardware. Results presented in this paper show that automated ASEP designs can be more than an order of magnitude smaller, and therefore more power efficient, than equivalent general-purpose application-specific embedded processors. The small size results from the fact that both the architecture and instruction set of an ASEP are tailored to the unique needs of a particular application within the embedded system. The automation approach presented in this paper helps to reduce the high design costs and necessity to use highly skilled design engineers. An experimental tool named SYNAPSE has been developed and experiments presented in this paper indicate that extreme savings in terms of hardware resources can be achieved, yielding results better by more than order of magnitued in terms of size, an therefore also power consumption, compared with state-of-the-art synthesis of reconfigurable general-purpose embedded architectures such as those based on RISC-V and NIOS. The proposed approach has a potential to make use of artificial intelligence and machine learning to use the available expert knowledge in the automation.