The field of hardware acceleration for AI-driven applications is moving towards the development of more efficient and versatile architectures. Recent research has focused on designing accelerators that can support multiple dataflows, precision modes, and sparsity formats, enabling better performance and energy efficiency. Another trend is the exploration of approximate computing methods, which can improve hardware complexity, latency, and energy consumption in error-resilient applications. Additionally, there is a growing interest in open-source and configurable hardware platforms, such as RISC-V GPUs, which can be optimized for ultra-low-power edge devices. Noteworthy papers include: FlexNeRFer, which introduces a multi-dataflow, adaptive sparsity-aware accelerator for on-device NeRF rendering, and e-GPU, which presents an open-source and configurable RISC-V GPU platform for TinyAI applications. MINIMALIST also presents a streamlined and hardware-compatible architecture for efficient in-memory computation of gated recurrent units. SEGA-DCIM proposes a design space exploration-guided automatic digital CIM compiler with multiple precision support, offering solutions with wide design space and competitive performance.