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Over the last decade, there has been an immense interest in the quest for emerging memory technologies which possess distinct advantages over the traditional silicon-based memories. The unique opportunities ushered by these technologies such as high integration density, near-zero leakage, non-volatility and, in some cases, excellent CMOS compatibility, has triggered the development of design techniques, enhancing the computation capabilities of various systems. Further, in the era of big data, the emerging memory technologies offer new design opportunities to address a pressing need of achieving close integration of logic and memory sub-systems with an objective to overcome the von-Neumann bottleneck associated with the humungous cost of data transfer between logic and memory. Such a logic-memory coupling not only enables low power computation in standard systems, but also promises high energy efficiency in unconventional compute platforms such as the brain-inspired deep neural networks (DNNs) which have transformed the field of machine learning (ML) in recent years. However, in order to exploit the unique properties of the emerging memory technologies for efficient logic-memory integration, there exists a strong need to explore cross-layer design solutions which can potentially enable efficient computation for current and future generation of systems. Motivated by this, in this dissertation, we harness the benefits offered by the emerging technologies and propose novel devices and circuits which exhibit an amalgamation of logic and memory functionalities. We propose two variants of memory devices: (a) Reconfigurable Ferroelectric transistors (R-FEFET) and (b) Valley-Coupled-Spin Hall (VSH) effect based magnetic random-access memory (VSHMRAM), which exhibit unique logic-memory unification. Exploiting the intriguing features of the proposed devices, we carry out a cross-layer exploration from device-to-circuits-to-systems for energy efficient computing. We investigate a wide spectrum of applications for the proposed devices including embedded memories, non-volatile logic, compute-in-memory circuits and artificial intelligence (AI) systems. The first technology of our focus is ferroelectric transistor (FEFET), which is being actively explored for logic and memory applications. Experimental studies have showcased volatile (logic) or non-volatile (memory) characteristics for FEFET by employing static/design time optimizations. However, if run-time tuning of non-volatile and volatile modes can be achieved, several new avenues for circuit design will open. Inspired by this, we propose Reconfigurable FEFET (RFEFET), which has the capability to dynamically modulate its operation between volatile and nonvolatile modes, enabling true logic-memory synergy at the device level. Utilizing these unique features of the R-FEFET, we propose an embedded non-volatile flip-flop design (R-NVFF) featuring a fully automatic backup operation (during power shut down) without the need of any external circuitry or signals. Compared to a previously proposed FEFET based NVFF, the proposed R-NVFF exhibits 69% lower check-pointing energy (which includes backup and restore operation). We also propose non-volatile memory (NVM) with highly energy-efficient read and write operations enabled by the dynamic reconfigurability in R-FEFETs. Our proposed NVM exhibits 55% lower write power, 37%-72% lower read power and 33% lower area compared to an FEFET-NVM. Finally, we implement the proposed NVM and R-NVFF in a state-of-the-art intermittently-powered platform and show up to 40% energy savings at the system-level. Another technology, which has sparked immense interest in spintronic applications, is the Valley-coupled-Spin Hall (VSH) effect in two-dimensional Transition Metal Dichalcogenides (2D TMDs). The unique generation of out-of-plane spin currents in monolayer TMDs can potentially enable efficient switching of nano-magnets. In this dissertation, we propose an emerging spin-based memory device featuring close logic-memory integration utilizing the VSH effect in 2D TMD transistors, where the information is stored in nano-magnets (which are unified with the transistor), for energy efficient computing. We propose two variants of NVM designs, namely single-ended VSH-MRAM and differential DVSH-MRAM. We show that the integrated gate feature exclusive to 2D TMDs, facilitates access transistor-less memory array designs, resulting in ultra-high integration density. We compare the proposed memory designs with the standard Giant Spin Hall (GSH) effect-based memories and showcase 35%-67% lower energy consumption at the circuit-level and up to 3.14X energy efficiency at the system-level in the context of general purpose computing systems as well as targeted system applications such as energy harvesting platforms. In addition to traditional computing architectures, the logic-memory synergy in the proposed device technologies, showcase an immense potential for energy-efficient in-memory computation, especially for AI specific hardware running DNN/ML algorithms. We propose R-FEFET and DVSH-MRAM based design of novel compute-enabled memory fabrics. We custom design memory bit-cells which enable massively parallel Boolean and non-Boolean in-memory 23 computations using minimal array accesses. For example, we propose R-FEFET and DVSHMRAM based NVM cells which performs natural and simultaneous computation of bit-wise Boolean AND and NOR logics in a single array access. We also propose a compact compute module, attached to the array peripherals, for carrying out other logic and arithmetic operations such as addition. The proposed in-memory computation technique shows up to 71% lower energy consumption compared to existing FEFET and GSH-MRAM based compute-in-memory solutions. Moreover, for targeted energy-autonomous system workloads, we propose application-specific, FEFET inspired CiM fabric, which demonstrate 32X and 40X improvement in energy consumption and performance during edge-sensing, when compared to conventional computing architectures. Lastly, for energy-efficient computing in edge devices, we propose compute-enabled memory cells with ternary-precision, which achieves a sweet spot between accuracy and energy-efficiency for DNN workloads. With optimal encoding scheme for the computing elements in synergy with device-circuit co-design, we achieve efficient ternary in-memory dot-product computation with minimal number of transistors per cell. The proposed ternary compute-in-memory arrays show up to 3.4X reduction in energy and 7X improvement in performance when compared to optimized near-memory DNN accelerators. Overall, evaluation results of the proposed CiM techniques in this dissertation, show significant reduction in system energy along with system performance improvement over conventional von-Neumann architecture-based approaches for a wide range of application workloads, thus addressing the critical need for energy efficient logic-memory synergy in future computing platforms.