Neuromorphic systems are of highly importance in artificial intelligence (AI) to eschew challenges inherent to deep learning acceleration in conventional systems. Throughout the development history of neuromorphic systems, analog in-memory computing with emerging memory technologies, such as resistive random-access memory (RRAM), offer advantages by executing operations in situ, exactly where the data are located, providing significant improvement in data throughput and energy efficiency. Herein, we introduce a temporal-encoded memristive crossbar that offers a faster yet more efficient in-memory operations to accelerate deep learning algorithms. Specifically, the memristive crossbar is built based upon the hafnium-oxide RRAM in a 6-transistor-1-RRAM (6T1R) structure, controlled by a series of temporal-encoded voltage pulses. With a bidirectional switching mechanism, the 6T1R cell can be programmed to support multi-bit operation and represent either positive or negative weight values per single cell. More importantly, the 6T1R cell can be set directly to the high-impedance state, either representing zero or quarantining faulty RRAM from the network regardless of its resistance state. Proof-of-concept simulations are conducted using a custom 65 nm CMOS/RRAM technology node, yielding an average classification accuracy of 94%, 95%, and 99%, respectively, when tested with the banknote authentication, balance scale, and iris plant dataset. Better yet, the parallel inter-tile communications in analog achieves up to 1.6 trillion-operations per second.