Most of the connected devices produce streaming data. Generally data is continuous and has a temporal characteristics to it. Data becomes valuable when data is used to produce insights and decisions in almost real time.
Streaming data is processed sequentially and incrementally to produce insights in real time. RAPTM processes small batches of data very efficiently with almost zero latency. Unlike all other solutions, which focus on alerts, RAPTM provides actionable insights for streaming data.
Search is life-blood of online businesses. Be it search engine, e-commerce website or a travel site, users want to get answers instantly and a good answer means a returning customer and a bad answer means a lost customer.
Technology should be able to run small batches as waiting to make large batches means increasing latency for the earlier users.
RAP’s agent based compute is very efficient with the large and small batches. RAPTM’s architecture makes it ideal to accelerate conditional compute.
Genomics, Life Sciences, Trading and Weather forecasting applications need high performance computing for their operations. These workloads require high compute, parallelism and complex modeling. Some of these workloads require thousands of nodes to be connected.
RAPTM’s agent based architecture provides huge parallelism with SIMA groups. Agents in these groups parallelize computations, run asynchronously and provide acceleration that is not possible with competitive solutions.
Businesses are constantly increasing customer engagements and evaluating customer responses for their campaigns. Globalization has made business value chain very complex and complex financial tools require continuous monitoring.
RAPTM has on-chip agents to expedite decision making. Furthermore, RAPTM’s Agents based Learning Techniques can be used for text mining, and RAPTM based dialog systems effectively learn from user interactions. RAPTM can predict and optimize outcomes and simulate potential alternatives in a typical business setting.
The three pillars powering autonomous vehicles are Perception, Prediction and Decision.
RAPTM provides the industry’s best performance for Deep Learning deployment and on-chip agents perform action prediction, path planning and driving policies. These agents focus on different tasks and compute them parallelly thereby increasing effectiveness of hardware. RAPTM’s on-chip agents learn decision making by trial and error and enable autonomous vehicles. RAPTM can enable level 3, level 4 and level 5 vehicles.
Commercial Drones that are used in geospatial surveys, construction monitoring, product delivery and securing critical assets operate in extreme areas and collect huge amounts of HD video data.
RAPTM provides intelligence in drones making them autonomous – fueling them with perception, prediction and decision-making capabilities
By analyzing video data in real time RAPTM provides actionable insights preventing the need to transfer huge quantities of data to the Cloud.
The robots which are revolutionizing eCommerce and many other industries are required to discover optimal behavior autonomously through trial and error, making DRL the appropriate AI for Robots. Robots need abilities to avoid obstacles in the path and completely their tasks.
RAPTM’s ability to support Deep Learning based perception and agent based decision making is the right hardware to enable commercial robots.
Today Industries are sitting on mountains of raw data, which can aid in intelligent predictions, optimize systems and make processes autonomous. Few areas where AI can impact are Predictive maintenance, fault detection, HVAC automation, fleet logistics and routing and warehouse automation.
RAP’s Agents can be used for predictions and optimization and automation of processes respectively. RAPTM is capable of crunching millions of data points to provide apt predictions and there always remains the scope of continuous learning and optimization through trial and error.
Humans love to talk and AI powered voice interfaces have now enabled human interaction with machines.
A low power RAPTM integrated in the voice interfaces would understand conversation context and will result in a better query to the cloud. This will also reduce delays. Agents in RAPTM will be able to separate human voice from the environmental noise, providing better voice signal for dialog management.
Augmented, Virtual and Mixed Reality(AVMR) technologies are the next wave of computing platform. Gaming, health care, warehouse, entertainment, education and training are some of the industries that will be impacted by these technologies.
Low power RAPTM can identify objects with Deep Learning, and agent based computing provides immersive experiences to users. RAPTM has industry’s best CNN, LSTM inference speeds thereby making RAPTM an ideal hardware to design next generation AVMR systems.
Surveillance Cameras are not only meant to record what is happening, but should also be able to identify the scene to take appropriate action in real time to save lives and protect people.
Low power RAPTM in these surveillance cameras can identify the slightest activity and can provide actionable insights based on the situation. On-chip agents in RAPTM can also manage different stages in a surveillance system and can take appropriate actions.
Handheld devices with sensors perceive objects and listen to sounds. We can talk to these devices and these devices can find answers to our questions.
When these devices become intelligent, these devices can monitor people’s vitals and inform physicians when they see an anomaly. These devices can also become teachers interacting with kids by enhancing their curiosity and answering their questions. These devices will not send all the data to the clouds but only send insights to clouds that will help in long term trend monitoring. RAPTM can provide intelligence required in these devices.
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