  In the past decades, there has been a significant increase in sensor density
and sensor deployment, driven by a significant miniaturization and decrease in
size down to the chip level, addressing ubiquitous computing, edge computing,
as well as distributed sensor networks. Material-integrated and intelligent
systems (MIIS) provide the next integration and application level, but they
create new challenges and introduce hard constraints (resources, energy supply,
communication, resilience, and security). Commonly, low-resource systems are
statically programmed processors with application-specific software or
application-specific hardware (FPGA). This work demonstrates the need for and
solution to virtualization in such low-resource and constrained systems towards
resilient distributed sensor and cyber-physical networks using a unified
low-resource, customizable, and real-time capable embedded and extensible stack
virtual machine (REXAVM) that can be implemented and cooperate in both software
and hardware. In a holistic architecture approach, the VM specifically
addresses digital signal processing and tiny machine learning. The REXAVM is
highly customizable through the use of VM program code generators at compile
time and incremental code processing at run time. The VM uses an integrated,
highly efficient just-in-time compiler to create Bytecode from text code. This
paper shows and evaluates the suitability of the proposed VM architecture for
operationally equivalent software and hardware (FPGA) implementations. Specific
components supporting tiny ML and DSP using fixed-point arithmetic with respect
to efficiency and accuracy are discussed. An extended use-case section
demonstrates the usability of the introduced VM architecture for a broad range
of applications.
