Filamentary metal–oxide RRAM conducts through a tunneling gap for which the current grows exponentially with bias, so its Read-range I–V characteristic follows a $\sinh (V/V_0)$ law. This introduces a systematic, input-dependent multiply–accumulate (MAC) error when used in analog Compute-in-Memory (CiM) accelerators with inputs encoded as voltage amplitudes. Because the nonlinearity also varies with resistance state, a single correction factor cannot generally compensate for all programmed weights. We present a measurement-calibrated device-to-circuit study of this error and show how it can be mitigated by replacing voltage-amplitude inputs with fixed-bias time-domain inputs, as in Asynchronous Stream Computing (ASC), which uses constant-amplitude sigma–delta streams. RRAM samples from two independent processes, Pt/Ti/HfO₂/Ta/Pt and Al/TiO₂/Au/Ti, are characterized, and the Stanford compact model is calibrated to each process. Together with two published parameter sets, the fitted nonlinearity coefficient $V_0$ spans 0.29–$0.55$ $\mathrm{V}$ across four stacks and varies with resistance state within individual devices. In 65 $\mathrm{n}$$\mathrm{m}$ circuit simulations, voltage-amplitude encoding incurs up to 30% relative error in a single cell and approximately 8.5% full-scale-normalized error in single-bitline crossbars of 4 to 256 rows. At a 5 $\mathrm{m}$$\mathrm{s}$ accumulation window, ASC reduces the array error to 0.4–0.8% with ideal-integrator readout and 0.8–2.3% with transistor-level A$\mathrm{\Sigma }\mathrm{\Delta }$M readout. Under measured device variability, group-balanced Monte Carlo simulations yield 7.6–9.2% analog error versus 0.5–0.7% ASC error. Wire resistance reduces the fixed-bias advantage but does not eliminate it within the simulated conditions. First-order energy estimates identify accumulation time and cell read current as important costs, while practical implementation also requires wordline drivers and high-ratio current division.