  Battery-powered IoT devices face challenges like cost, maintenance, and
environmental sustainability, prompting the emergence of batteryless
energy-harvesting systems that harness ambient sources. However, their
intermittent behavior can disrupt program execution and cause data loss,
leading to unpredictable outcomes. Despite exhaustive studies employing
conventional checkpoint methods and intricate programming paradigms to address
these pitfalls, this paper proposes an innovative systematic methodology,
namely DIAC. The DIAC synthesis procedure enhances the performance and
efficiency of intermittent computing systems, with a focus on maximizing
forward progress and minimizing the energy overhead imposed by distinct memory
arrays for backup. Then, a finite-state machine is delineated, encapsulating
the core operations of an IoT node, sense, compute, transmit, and sleep states.
First, we validate the robustness and functionalities of a DIAC-based design in
the presence of power disruptions. DIAC is then applied to a wide range of
benchmarks, including ISCAS-89, MCNS, and ITC-99. The simulation results
substantiate the power-delay-product (PDP) benefits. For example, results for
complex MCNC benchmarks indicate a PDP improvement of 61%, 56%, and 38% on
average compared to three alternative techniques, evaluated at 45 nm.
