Chip Modelling and Design

Ongoing collaboration with arm has enabled Ainira Industries to developing its Gen-4 AI Chip with the following characteristics and performance:

Hybrid Architecture — authentic chip architecture combines a high-performance CPU and a high-bandwidth GPU, used for general-purpose computing and graphics rendering, respectively, and a custom AI accelerator that can be optimised for artificial intelligence workloads.

Memory Hierarchy — chip boasts a hierarchical memory architecture that includes a large on-chip SRAM, used for fast data access and cache, and a high-bandwidth on-package HBM and a high-speed off-chip DDR5 memory, which are used for larger data sets and long-term storage.

Hardware Accelerators — latest chip design will include specialised hardware accelerators for AI workloads, such as tensor processing units (TPUs) and digital signal processors (DSPs) that can be assigned to specific tasks, such as matrix multiplication, convolution, and signal processing.

Dynamic Voltage and Frequency Scaling — based on the workload, this design feature dynamically adjusts the voltage and frequency of the chip, thus reducing its power consumption.

Power Gating — through clock and/or voltage gating, this design feature turns off unused parts of the chip to increasing its power usage efficiency.

Network-on-Chip — this design feature provides a scalable and efficient communication infrastructure, enabling high-bandwidth and low-latency communication between different components of the chip, and reducing its power consumption while improving overall performance.

Robust Communication — PCIe/CCIX high-speed semiconductor interfaces enable real-time communication and coordination between devices; additionally, they support advanced networking protocols, such as Ethernet and 5G/6G, which enable communication between network, edge devices, and the cloud.

Software Ecosystem — semiconductor DevOps are supported by a robust software ecosystem including development tools, libraries, compilers, and frameworks for AI applications like TensorFlow, PyTorch, and Keras.

Upgradable Design — unique multi-chip module (MCM) architecture enables different components of the chip, such as the CPU or GPU, to be upgraded independently.

High-End Security — hardware-embedded security features, such as encryption and secure boot, will protect against unauthorised access and cyber-attacks.

Process Technology — semiconductors will be fabricated using a 2nm or lower process technologies to enable high performance and energy efficiency; ongoing engagement with Tier 1 (TSMC and AMSL) and Tier 2 Fabs.

Advanced Packaging Technologies — to improve its performance (higher bandwidth, lower latency), energy efficiency, and fabrication output (e.g. integrating multiple chips onto a single package), Ainira semiconductors will make use of advanced 2.5D/3D packaging technologies.

Prospective Partnerships

Established companies like Nvidia, Intel, and AMD work closely with smaller but innovative players in the industry to optimise chip designs for AI tasks, aiming for higher performance and efficiency. This collaboration often includes joint research, technology licensing, and co-development of specialised hardware. The goal is to push the boundaries of AI capabilities while meeting the demands of various applications.

NVIDIA

Specialised AI Hardware — Nvidia is a leader in designing specialised hardware for AI, particularly GPUs, such as those in the GeForce, Quadro, and Tesla series, which are optimised for parallel processing, making them highly efficient for deep learning tasks.

CUDA Architecture — Nvidia's CUDA is a parallel computing platform and programming model that enables developers to harness the power of Nvidia GPUs for general-purpose computing. CUDA allows for efficient parallelisation of AI algorithms, leading to faster training and inference times.

Deep Learning Software Stack — Nvidia provides a comprehensive software stack for deep learning, including libraries like cuDNN and cuBLAS, as well as frameworks like TensorFlow, PyTorch, and MXNet optimised for Nvidia GPUs. This software ecosystem simplifies AI development and optimisation on Nvidia hardware.

AMD

High-Performance Computing — AMD's CPUs and GPUs are known for their high performance, making them suitable for AI tasks such as training deep learning models and running complex simulations; AMD's Ryzen CPUs and Radeon GPUs provide strong computational power at competitive prices.

Accelerated Computing — AMD's GPUs, such as the Radeon Instinct series, designed to accelerate AI workloads, offer parallel processing capabilities optimised for tasks like deep learning training and inference, enabling faster computation times.

Open Standards and Interoperability — AMD is committed to open standards, making its hardware compatible with a wide range of software tools and frameworks; this interoperability simplifies the development process and allows for flexibility in choosing software solutions.

Heterogeneous Computing Solutions — AMD offers heterogeneous computing solutions that combine CPUs and GPUs to maximise performance and efficiency, an approach that is beneficial for AI workloads, as it allows for distributed computing and parallel processing across multiple types of processors.

INTEL

Diverse Product Portfolio — Intel provides a wide range of products, including CPUs, FPGAs (Field Programmable Gate Arrays), and AI accelerators like the Intel Movidius VPU (Vision Processing Unit) and Nervana Neural Network Processor (NNP). AI acceleration is built into every Intel® Core™ Ultra processor.

Deep Learning Framework Optimisation — to enable faster training and inference times, Intel optimises popular deep learning frameworks like TensorFlow, PyTorch, and MXNet for its hardware, ensuring efficient utilisation of resources and performance improvements.

Advanced Manufacturing Process — Intel has advanced semiconductor manufacturing capabilities, which result in high-performance chips with lower power consumption.