Analog AI: What Is It?
Analog AI, or analog in-memory computing, is the technique of encoding data as a physical quantity and performing computations using the physical properties of memory devices. It is an energy-efficient deep learning training and inference technique.Analog AI characteristics
Memory that is not volatile
Analog AI makes use of non-volatile memory chips, which can store data for up to 10 years without electricity.Memory-based computing
Analog AI stores and processes data in the same place, eliminating the von Neumann bottleneck that limits calculation speed and efficiency.Analog depiction
Analog AI uses the physical properties of memory devices to carry out matrix multiplications in an analog manner.Arrays of crossbars
In analog artificial intelligence, synaptic weights are locally stored in the conductance values of nanoscale resistive memory devices.Minimal energy usage
In analog artificial intelligence, synaptic weights are locally stored in the conductance values of nanoscale resistive memory devices.
Overview of Analog AI
Improving Deep Neural Network systems' performance and energy efficiency.Analog in-memory computing can be used for two different deep learning tasks: training and inference. The first step is to train the models using a dataset that has been tagged frequently. For instance, if you want your model to identify different images, you would provide a set of annotated photos for the training task. After the model is trained, it can be used for inference.
Instruction Like most computing these days, AI models are digital processes run on traditional computers with traditional architectures. These systems first move data from memory onto a queue before sending it to the CPU for processing.
AI training may require large amounts of data, all of which must go through the queue before being supplied to the CPU. This results in what is referred to as "the von Neumann bottleneck," which can drastically lower computation speed and efficiency. IBM Research is exploring ways to train AI models faster and with less energy if the bottleneck brought on by data queuing is removed.
These technologies are analog, which means they record information as a tangible, changeable thing, like the wiggles in the grooves of vinyl records. It is looking into two types of training devices: resistive random-access memory (RRAM) and electrochemical random-access memory (ECRAM). Both devices have the ability to process and store data. Jobs may now be finished much faster and with far less energy because data is no longer transmitted from memory to the CPU via a queue.
Inference is the process of making a conclusion based on information that is already known. This process is simple for humans to perform, but inference is expensive and slow for machines. An analog approach is being used by IBM Research to address that challenge. Analog may bring to mind Polaroid Instant cameras and vinyl LPs.
Digital data is represented by lengthy sequences of 1s and 0s. Record grooves are an example of a moving physical property that represents analog information. Phase-change memory (PCM) is the central component of its analog AI inference computers. This highly flexible analog technique computes and stores information via electrical pulses. The chip is hence far more energy-efficient.
It uses PCM as a synaptic cell and is an AI term for a single unit of weight or information. We are able to build a large physical neural network that is loaded with pretrained data, which is prepared to jam and infer on your AI workloads, thanks to the placement of over 13 million of these PCM synaptic cells in an architecture on the analog AI inference chips.
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