AI acceleration

Extracted Attributes

• Primary Goal

  Speeding up AI computation and reducing the sizes of required models and data structures

• Enterprise Impact

  Intensification of work for knowledge workers and emergence of corporate token budgets

• Hardware Efficiency

  100-1,000x more efficient than general-purpose compute machines

• Key Hardware Components

  GPUs, NPUs, FPGAs, and integrated AI accelerators in CPUs

• Projected Data Center Investment

  Trillion-dollar scale by the end of the 2020s

• Latency Requirement (Autonomous Navigation)

  20 microseconds

• Latency Requirement (Voice/Video Assistants)

  10 microseconds

Timeline

• Beginning of the decade of AI Super-Acceleration, marked by a shift from general-purpose silicon to domain-specific AI chips. (Source: UX Tigers)

  2019-01-01

• The All-In Podcast discusses AI acceleration trends, including the rise of token budgets and the UC Berkeley study on knowledge worker intensification. (Source: Document cf48e3fb-2b33-4f1c-ad80-081047fdee62)

  2024-02-11

• Projected timeframe for the emergence of trillion-dollar data centers and the stabilization of AI-specific chip design advances. (Source: UX Tigers)

  2029-12-31

Hailo Technologies

Hailo Technologies Ltd. is an Israeli technology company specializing in designing and manufacturing AI processors and AI accelerator used in autonomous vehicles, security cameras, autonomous mobile robots, and the like. Headquartered in Tel Aviv and with seven international offices, the company operates in North America, Europe, and Asia.

Web Search Results

• Types of AI Acceleration in Embedded Systems

  This involves two approaches: selecting the right hardware architecture to accelerate AI, and designing the right models that require lower compute workloads. The former is the traditional approach to AI acceleration; it involved implementing computation in parallel with progressively larger processors (and eventually GPUs) in order to keep compute times low. The latter is newer and is being enabled by software-based approaches that reduce workloads without requiring larger processors. [...] ## AI Acceleration Happens in Hardware and Software The goal in AI model acceleration is two-fold: to reduce the size of models and data structures involved in AI computation, and to speed up inference to produce useful results. The same idea applies in training. Throughout the majority of AI history, hardware acceleration was rudimentary, basically involving throwing computing resources at problems until the computation time became reasonable. Now that so much research attention has shifted to neural network development, software-level acceleration techniques are also commonplace and are aided by open-source/vendor libraries and code examples. [...] # Types of AI Acceleration in Embedded Systems Author Cadence PCB Solutions The biggest challenge to broader commercialization of AI has been its computational speed required to implement inference and training with a particular model and set of data structures. AI acceleration aims to solve the problem of speeding up AI computation and reducing the sizes of required models and data structures, thereby increasing the computational throughput and frequency in real applications.

• What is an AI accelerator?

  ### Types of AI accelerators AI accelerators are divided up into two architectures based on their function: AI accelerators for data centers and AI accelerators for edge computing frameworks. Data center AI accelerators require highly scalable architecture and large chips, such as the Wafer-Scale Engine (WSE), built by Cerebras for deep learning systems, while AI accelerators built for edge computing ecosystems focus more on energy efficiency and the ability to deliver near real-time results. Wafer-scale integration [...] ## How do AI accelerators work? Due to their unique design and specialized hardware, AI accelerators boost AI processing performance considerably when compared to their predecessors. Purpose-built features enable the solving of complex AI algorithms at rates that far outpace general-purpose chips. AI accelerators are typically made from a semiconductor material, like silicon, and a transistor that’s connected to an electronic circuit. Electrical currents running through the material are turned on and off, creating a signal that is then read by a digital device. In advanced accelerators, the signals are switched on and off billions of times per second, allowing the circuits to solve complex computations using binary code. [...] ### AI accelerators need more power than their size allows AI accelerators are small, most are measured in millimeters and the largest in the world is only about the size of an iPad, making it difficult to direct the amount of energy needed to power them into such a small space. This has become increasingly difficult as compute demands from AI workloads have risen in recent years. Advancements will need to be made soon in the power delivery network (PDN) architectures behind AI accelerators or their performance will start to be affected. ## How do AI accelerators work?

• Artificial Intelligence (AI) Accelerators – Intel

  By AI accelerators are used to unlock advanced AI performance. Discrete hardware accelerators are commonly employed to enable parallel computing for demanding AI tasks, working in tandem with the CPU to meet sizable computational demands. Integrated AI accelerators are built-in capabilities included in today’s CPU technologies to enable cost-effective AI via lean, CPU-only architectures. Applying both types of AI accelerators is critical to supporting demanding AI workloads from edge to cloud. [...] ## AI Accelerators Solutions The increasing adoption of AI means that AI accelerators are being deployed at virtually every layer of the technology landscape: For end user devices, GPUs and integrated NPUs are commonly used to boost AI workload performance. At the edge, FPGAs offer flexibility and efficiency benefits that can help extend AI capabilities to more places. In the data center, both GPUs and purpose-built AI accelerators are being used at scale to power extremely complex AI workloads like financial modeling and scientific research. Integrated AI accelerators are available in select CPU offerings, with options available across edge, data center, cloud, and client computing. [...] To meet these emerging demands, technologists take advantage of AI accelerators, which can be either discrete pieces of hardware incorporated into their solution design or built-in features in the CPU. Both forms of AI accelerators provide supercharged performance for AI workloads. They’re employed across today’s IT and AI landscape, with use cases in client computing devices, edge environments, and data centers of all types. Discrete hardware AI accelerators are most commonly used alongside CPUs in the parallel computing model, though select technologies can also be used in stand-alone architectures. Some single-package CPU/accelerator offerings are also available on the market.

• The Decade of AI Super-Acceleration - UX Tigers

  The reason we currently have unsustainable acceleration is that truly big investments in AI didn’t start until recently. Raw AI compute currently accelerates far faster than standard computing, because we’re suddenly willing to invest hundreds of billions of dollars in building AI data centers for training and inference compute. Aschenbrenner expects trillion-dollar data centers toward the end of the 2020s, but we’re unlikely to go much further than investing at the level of tens of trillions of dollars in AI hardware. (The world’s entire GDP is USD $140 T in PPP, and even in the likely case that AI doubles world GDP, that will leave maybe $50 T for AI investments, since most of our added wealth should be spent on improving humanity’s standard of living.) [...] AI raw compute also accelerates faster than old-school computers because companies like NVIDIA have started designing AI-specific chips. For the first several generations of custom-designed silicon, domain-specific chips can wring out a lot of efficiencies compared to general-purpose silicon. But eventually, advances in AI-specific chip-design should slow to approximately the pace characterized by Moore’s Law. (Still improving every year, but not by nearly as fast as we enjoyed during the decade 2019-2029.) [...] On the algorithm side, only very few of the world’s top geeks used to work on AI, since it was considered to be a perennial loser topic. Now, the situation is the opposite. Anybody who’s any good in technology now wants to work on AI, with the losers left to work on non-AI projects. However, this shift from AI attracting only a few top talents to attracting all the top talents can only happen once. This change is also happening in the current decade, so we’re enjoying an additional acceleration in algorithmic efficiency from putting better talent on the job.

• What is an AI Accelerator? – How It Works - Synopsys

  As intelligence moves to the edge in many applications, this is creating greater differentiation in AI accelerators. The edge offers a tremendous variety of applications that requires AI accelerators to be specifically optimized for different characteristics like latency, energy efficiency, and memory based on the needs of the end application. For example, while autonomous navigation demands a computational response latency limit of 20μs, voice and video assistants must understand spoken keywords in less than 10μs and hand gestures in a few hundred milliseconds. [...] ## Definition An AI accelerator is a high-performance parallel computation machine that is specifically designed for the efficient processing of AI workloads like neural networks. Traditionally, in software design, computer scientists focused on developing algorithmic approaches that matched specific problems and implemented them in a high-level procedural language. To take advantage of available hardware, some algorithms could be threaded; however, massive parallelism was difficult to achieve because of the implications of Amdahl’s Law. ## How Does an AI Accelerator work? There are currently two distinct AI accelerator spaces: the data center and the edge. [...] Energy efficiency. AI accelerators can be 100-1,000x more efficient than general-purpose compute machines. Whether they’re used in a data center environment that needs to be kept cool or an edge application with a low power budget, AI accelerators can’t afford to draw on too much power or dissipate too much heat while performing voluminous amounts of calculations. Latency and computational speed.Thanks to their speed, AI accelerators lower the latency of the time that it takes to come up with an answer. This low latency is especially important in safety-critical applications like advanced driver assistance systems (ADAS), where every second counts.