Boosting AI capabilities at the periphery relies on appropriate processors and memory solutions
The rapid advancement of artificial intelligence (AI) technology has opened up a world of possibilities, particularly for low-power edge devices. To fully harness the potential of AI in these devices, a balanced approach is required that focuses on the synergy of AI accelerator processors and Low-Power DDR (LPDDR) memory.
Specialised AI Accelerator Processors
The use of AI-oriented vision processing units (VPUs), neural processing units (NPUs), or AI System-on-Chips (SoCs) designed from the ground up for edge inference tasks is a key strategy. These chips offer high TOPS/W (tera operations per watt) and are optimized to run multiple deep learning models efficiently and concurrently on-site without cloud dependency.
Low-Power DDR Memory
Pairing AI accelerators with LPDDR memory delivers high-bandwidth data transfer critical for AI workloads while minimizing power consumption. LPDDR4X, for example, supports extreme temperature ranges and delivers high throughput without sacrificing energy efficiency, ideal for automotive, industrial, and enterprise edge systems.
Hardware-Memory Synergy
Tight integration between AI processors and LPDDR memory ensures accelerated data access and reduced latency, enabling real-time AI processing. This synergy supports applications like AI-powered video analytics in smart cameras, where on-device image enhancement and complex inferencing run efficiently.
Model Optimization Techniques
To further reduce computational load and memory demand, models are often optimized by quantization (reducing precision), pruning (removing redundant parameters), and knowledge distillation (creating smaller but equally accurate models). Smaller models lighten memory bandwidth requirements and accelerate inference speed, complementing hardware efficiency.
System-Level Optimization
Lightweight software orchestration platforms like KubeEdge or K3s enable managing AI workloads through containerization and automated scaling, ensuring efficient resource usage on heterogeneous edge hardware.
This approach results in benefits such as reduced latency, lower power consumption, improved data security and privacy, and high-throughput AI capabilities. These benefits enable complex, real-time applications like autonomous driving, industrial IoT, and healthcare monitoring.
The Hailo-10H AI Processor
The Hailo-10H, with its unique, powerful, and scalable structure-driven dataflow architecture, enables edge devices to run deep learning applications at full scale more efficiently and effectively than traditional solutions, while significantly lowering costs.
The Future of AI on the Edge
Hailo processors are geared towards the new era of generative AI on the edge, in parallel with enabling perception and video enhancement through a wide range of AI accelerators and vision processors. Micron's LPDDR technology offers high-speed, high-bandwidth data transfer without sacrificing power efficiency, ideal for edge AI applications.
Inference involves data in motion, with the neural network requiring curated data that has undergone preprocessing, and post-processing being just as critical to the overall AI pipeline. Memory bandwidth constraints in embedded edge AI systems limit performance despite advances in model complexity and compute power.
As the growth of AI foundation models has led to a sharp increase in infrastructure and energy consumption costs, with a focus on data centre capabilities to support increasing demands of generative AI, the optimization of AI compute for low-power edge devices will continue to be a critical area of focus.
Technological advancements in specialized AI Accelerator Processors, such as AI System-on-Chips (SoCs), vision processing units (VPUs), or neural processing units (NPUs), offer high efficiency in running multiple deep learning models efficiently and concurrently on-site without cloud dependency. To complement this, pairing these chips with Low-Power DDR (LPDDR) memory delivers critical high-bandwidth data transfer for AI workloads while minimizing power consumption, as demonstrated by LPDDR4X's support for extreme temperature ranges and high throughput without compromising energy efficiency.
