Excellent recent EE article espousing the need and use of in memory and SNN AI.
Revolutionizing AI Inference: Unveiling the Future of Neural Processing
January 12, 2024
Virgile Javerliac
To overcome CPU and GPU limitations, hardware accelerators have been designed specifically for AI inference workloads, enabling highly efficient and optimized processing while minimizing energy consumption.
The AI industry encompasses a dynamic environment influenced by technological advancements, societal needs and regulatory considerations. Technological progress in machine learning, natural-language processing and computer vision has accelerated AI’s development and adoption. Societal demands for automation, personalization and efficiency across various sectors, including healthcare, finance and manufacturing, have further propelled the integration of AI technologies.
Additionally, the evolving regulatory landscape emphasizes the importance of ethical AI deployment, data privacy and algorithmic transparency, guiding the responsible development and application of AI systems.
The AI industry combines both training and inference processes to create and deploy AI solutions effectively. Both AI inference and AI training are integral components of the overall AI lifecycle, and their significance depends on the specific context and application. While AI training is crucial for developing and fine-tuning models by learning patterns and extracting insights from data, AI inference plays a vital role in utilizing these trained models to make real-time predictions and decisions. The growing importance of AI inference—more than 80% of AI tasks today—lies in its pivotal role in driving data-driven decision-making, personalized user experiences and operational efficiency across diverse industries.
Efficient AI inference implementation faces challenges concerning data availability, computational resources, algorithmic complexity, interpretability and regulatory compliance. Adapting to dynamic environments and managing scalability while controlling costs pose additional hurdles. Overcoming these challenges requires comprehensive strategies, including robust data management practices, advancements in hardware capabilities and algorithmic refinements. Developing explainable AI models and adhering to ethical and regulatory guidelines are crucial for building user trust and ensuring compliance. Furthermore, balancing resource allocation and cost management through efficient operational practices and technological innovations is essential for achieving sustainable and effective AI inference solutions across diverse industry sectors.
The pivotal role of AI inference
By automating tasks, enhancing predictive maintenance and enabling advanced analytics, AI inference optimizes processes, reduces errors and improves resource allocation. AI inference powers natural-language processing, improving communication and comprehension between humans and machines. Its impact on manufacturing includes predictive maintenance, quality control and supply chain management, fostering efficiency, reduced waste and enhanced product quality, highlighting its transformative influence on industry operations.
Industry challenges in sustainable AI inference
AI inference faces challenges concerning high energy consumption, intensive computational demands and real-time processing constraints, leading to increased operational costs and environmental impact. More than 60% of total AI power consumption comes from inference, and the increase of inference demands led to a 2.5× increase in data center capacity over two years (GAFA data). For servers, heat generation during intensive computations necessitates sophisticated cooling systems that further contribute to the overall energy consumption of AI processes.
Furthermore, balancing the need for efficient real-time processing with low-latency requirements, mandatory for servers, advanced driver-assistance systems (ADAS) or manufacturing applications, poses a significant challenge, requiring advanced hardware designs and optimized computational strategies. Prioritizing energy-efficient solutions—without compromising accuracy—with renewable energy sources and eco-friendly initiatives is crucial for mitigating the environmental impact of AI inference processes.
Classical AI inference hardware design, using a CPU or GPU, face limitations in achieving energy efficiency due to the complexity and specificity of AI algorithms, leading to high power consumption (hundreds of watts per multi-core units for servers). Inefficient data movement between processing units and memory further impacts energy efficiency and throughput; for instance, access to external DRAM consumes 200× more energy than access to local registers. At the end, and due to higher computational demands, next-gen servers using a CPU and GPU could consume up to 1,000 W by 2025. Deploying AI inference on resource-constrained, battery-powered devices is even more challenging, as the most efficient CPU- and GPU-based designs, with 10 mW to a few watts, suffer from strong throughput limitations, limiting AI complexity and the final user experience. Balancing energy efficiency with performance and accuracy requirements necessitates careful tradeoffs during the design process, calling for comprehensive optimization strategies. Inadequate hardware support for complex AI workloads can hinder energy efficiency and performance.
The search for energy-efficient solutions
The industry’s escalating demand for energy-efficient AI inference solutions is driven by sustainability goals, cost-reduction objectives and new usages.
Businesses seek scalable and high-performance solutions to manage complex AI workloads without incurring excessive energy costs. On the other hand, energy-efficient AI inference would enable mobile and resource-constrained devices to perform complex tasks without draining the battery quickly while reducing the reliance on cloud-based processing, minimizing data transmission and latency issues. It will contribute to enhanced user experiences through new usages with advanced features, such as real-time language translation, personalized recommendations and accurate image recognition, fostering greater engagement and satisfaction.
Innovative contributions in AI inference
To overcome CPU and GPU limitations, innovative hardware accelerators have been designed specifically for AI inference workloads, enabling highly efficient and optimized processing while minimizing energy consumption. Such accelerators implement an optimized dataflow with dedicated operators (pooling, activation functions, normalization, etc.) used in AI applications. The dataflow engine is the matrix-multiply unit, a large array of processing elements able to efficiently handle large matrix-vector multiplications, convolutions and many more complex operations, knowing the majority of neural networks are based on matrix-multiply operations.
To further optimize energy efficiency, AI accelerators have implemented new techniques, such as near-memory computing. Near-memory computing integrates processing elements within the memory subsystem, enabling faster data processing near the memory, thus reducing the energy consumption associated with data transfer. More recently, new approaches using “non-standard” techniques, such as in-memory computing or spiking neural networks (SNNs), are the most aggressive solutions to achieve highly energy-efficient AI inferences.
In-memory computing conducts computations directly within the memory, at circuit level, eliminating the need for data transfer and enhancing processing speed. The processing can be either performed in an analog or a digital way and implement different memory technologies, such as SRAM, flash or new NVM (RRAM, MRAM, PCRAM, FeFET, etc.). This approach is particularly beneficial for complex AI tasks involving large datasets. SNNs also represent an innovative approach to AI inference: They typically consist of interconnected nodes that communicate through spikes, enabling the simulation of complex temporal processes and event-based computations, which can be useful for tasks like processing time-sensitive data or simulating brain-like behavior.
Shaping the future of AI inference
AI accelerators leveraging near-/in-memory computing or SNNs offer significant impacts for the AI industry, including enhanced energy efficiency, improved processing speed and advanced pattern-recognition capabilities. These accelerators drive the optimization of hardware design, leading to the creation of specialized architectures tailored for specific AI workloads.
Additionally, they promote advancements in edge computing, facilitating efficient AI processing directly on edge devices and reducing latency. The transformative potential of these technologies highlights their crucial role in revolutionizing diverse industries, from healthcare and manufacturing to automotive and consumer electronics.
The integration of highly energy-efficient AI inference in healthcare and automotive sectors yields transformative impacts. In healthcare, it facilitates faster diagnostics and personalized patient care through rapid data analysis, leading to improved treatment outcomes and tailored medical interventions. Additionally, it enables the development of remote patient-monitoring systems, ensuring continuous health tracking and proactive intervention for individuals with chronic conditions. Moreover, in the realm of drug discovery, energy-efficient AI inference expedites the identification of potential drug candidates and accelerates pharmaceutical research and development processes, fostering innovation in medical treatments and therapies.
In the automotive industry, energy-efficient AI inference plays a crucial role in advancing safety features and autonomous-driving capabilities. It empowers vehicles with ADAS and real-time collision detection, enhancing overall road safety. Furthermore, it contributes to the development of self-driving technologies, enabling vehicles to make informed decisions based on real-time data analysis, thereby improving navigation systems and autonomous-driving functionalities. Additionally, the implementation of predictive maintenance solutions based on energy-efficient AI inference enables early detection of potential vehicle issues, optimizing performance, reducing downtime and extending vehicle lifespan.
Conclusion
The industry’s critical demand for energy-efficient AI inference solutions is driven by the need to promote sustainable operations, optimize resource utilization and extend device battery life. These solutions play a vital role in fostering eco-friendly practices, reducing operational costs and enhancing competitive advantages. By facilitating edge computing applications and minimizing energy consumption, energy-efficient AI inference solutions enable businesses to improve profitability, streamline processes and ensure uninterrupted functionality in mobile and IoT devices. Addressing this demand necessitates the development of energy-efficient algorithms and optimized hardware architectures heavily based on smart near-/in-memory computing techniques. Many new players come into the market with innovative computing solutions and the promise of running AI everywhere, from sensors to data centers, with the ambition of offering a completely new user experience.
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