Table 24.

Future research directions.

S/N	Key Areas for Future Research	Possible Research Focus
1	Computer Architecture	With the end of Dennard’s scaling and Moore’s law significantly slowing down, combined with the performance wall of Silicon, there remains an urgent need for innovation in the design and development of hardware architecture to meet the target for high-performance computing that face us today. In this respect, [176] suggests domain-specific architectures in the form of GPUs, FPGAs, TPUs, etc. as a research direction for future high-performance architectures. DSAs are different from ASICs in that DSAs introduce flexibility to the design due to their programmability. This is an open research area in computer architecture. Also, to generate highly efficient application-specific ASIC architecture designs, the design space is required to be explored efficiently, which is currently a stringent task carried out by human experts. However, [177,178] presents a frontier of employing machine learning techniques in exploring the design space at design time and runtime. Ruben et al. [179] proposed a machine learning-based prediction for the dynamic, runtime, and architectural optimizations of Embedded Systems. This is another open area of research—adopting machine learning in efficient computer architecture design and development
2	Deep Learning Optimizations	Deep learning models are computationally and memory intensive, and thus, implementing them within resource-constrained environments is tasking. There is, therefore, an opportunity for highly efficient optimization techniques to compress deep learning models efficiently with minimal or no accuracy loss. Although many research works have explored optimization techniques, optimization methods are infinite, and thus there remains an opportunity to optimize deep learning models still. Some optimization techniques are but are not limited to pruning, clustering, layer acceleration, quantization, and numeric precision. Some optimizations combine one or more of these techniques to compress a model successfully.
3	Hardware Security	Software security has been greatly explored, but little work has been done in hardware security which is a major concern in embedded systems development [180]. State-of-the-art embedded hardware architectures are prone to Trojan attacks, and thus, this creates the need for research in the design and development of secure embedded architectures for embedded applications.
4	Energy Efficiency and Power Management	Energy Efficiency is a critical issue in embedded computing systems because most embedded devices run on a battery [181]. Thus, to effect the continuous functionality of embedded devices, there remains the need to adequately design energy-efficient architectures and also adopt innovative power management techniques. This is a crucial research thrust in embedded computing technology, particularly to meet the requirements of high-performance machine learning applications [182,183,184].
5	Silicon Photonics	Current Silicon technology is reaching its limit for performance and thus [185] surveys the exploration of photonics-based architectures as a substitute for silicon technology owing to the high bandwidth and data transfer speed of photonics-based architectures [186,187]. This is a key research direction for high-performance architectures