- Technology has played a pivotal role in the COVID-19 crisis, with Artificial Intelligence assisting in dealing with the pandemic.
- Scope for AI is massive, with substantial progress made in the fields of self-driving cars, gameplaying machines and virtual assistants.
- Areas with significant improvement include image understanding, intelligent decision making, artificial creativity, natural language processing and physical automation.
- After the recent introduction of AKD1000, AI solutions provider BrainChip entered a joint development pact with Valeo Corporation.
- The Company also signed an agreement with a Detroit-based company for the evaluation of AkidaTM Neural Processor.
Artificial Intelligence is expected to have a firm grip on the market in the upcoming future. A recent report published in May 2020 by the Australian Government highlighted that there is a variety of high-profile demonstrations of Artificial Intelligence and significant progress has been made in fields of self-driving cars, gameplaying machines and virtual assistant. Further, AI has had a considerable role to play in managing the current COVID-19 crisis.
During the last decade, there have been five vital areas where significant growth has been witnessed. These include:
- Image understanding
- Intelligent decision making
- Artificial creativity
- Natural Language Processing
- physical automation
The scope of AI is not exhaustive. However, the above five regions have shown a significant change in the past ten years.
ASX-listed BrainChip Holdings Ltd (ASX:BRN) is one such technology company that is engaged in developing innovative neuromorphic processor that brings AI to the edge in a manner that is beyond the abilities of other neural network devices. The solution is high-speed, small, low power. It allows a broad range of edge abilities comprising continual learning, local training, and interpretation.
BRN, during April 2020, introduced its AKD1000 to spectators at the Processor Virtual Conference by the Linley Group. The AKD1000’s neural processor is capable of running a normal Convolutional neural network by transforming it into event-based, letting it to execute incremental learning and transfer it on a chip.
CNN or Convolutional neural network is a type of deep neural network used for analysing images. These have specially designed architecture that makes them comparatively easy to train, even for relatively deep networks.
After the introduction of AKD1000, BrainChip has recently signed two agreements, post which the Company noted a significant improvement in its share price in the past couple of weeks. BRN shares, which settled at A$0.058 on 22 May 2020 reached A$0.120 on 9 June 2020, representing a growth of ~106.9%.
On 9 June 2020, the share price skyrocketed after the release of the Company’s announcement related to its Joint agreement with Valeo Corporation. The stock settled at A$0.110 on 10 June 2020, down 8.333%.
Let us look at the two recent deals signed by the Company that led to the stock rally.
Joint Agreement with Tier-1 Automotive Supplier
On 8 June 2020, Brainchip Holdings Ltd entered a joint development pact using BRN’s Akida neuromorphic SoC with Valeo Corporation, a Tier-1 European automotive supplier of sensors as well as systems for Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles (AV).
Under the agreement, certain performance milestones & payments that are anticipated to include the Company’s expenditures. The term of the deal is specified by the accomplishment of performance goals & the accessibility of the Akida devices. Each party has the option to end the agreement for convenience with specific notification.
The confirmation of the Company’s Akida device by a Tier-1 supplier of sensors & systems to the automotive industry is believed to be significant progress.
In ADAS & AV applications, the real-time processing of data is vital for security as well as dependability of the autonomous systems. From the automotive industry, the suppliers, as well as the manufacturers, have acknowledged that the advanced & highly efficient neuromorphic nature of the Akida SoC makes it ideally fit to process data at the “Edge” for their advanced system solutions.
With the integration of the Akida neural network processor with sensors, the subsequent system can attain ultra-low power, min. latency, max. reliability & incremental learning.
The Akida neural processor’s game-changing high performance & ultra-low power utilisation allows smart sensor combination by resolving power and footprint difficulties for a range of sensor technologies. Further, it consumes less power than alternative AI solutions and simultaneously maintaining the necessary performance as well as accuracy in a fraction of the physical space.
Agreement with Ford Motor Company for the Evaluation of AkidaTM Neural Processor
On 24 May 2020, the Company signed a joint agreement with Detroit-based Company for evaluation of the Akida neural network System-on-Chip for Advanced Driver Assistance Systems & Autonomous Vehicle applications.
The evaluation agreement was binding on execution which was signed into with Ford Motor Company and is not the subject of a fixed term. The deal is based upon a partnership to assess Akida as it relates to the automotive industry & payments under the agreement proposed to cover related expenses and received periodically during the evaluation process.
Akida NSoC has an advanced and highly efficient neuromorphic nature, and the partners in the collaboration have also realised that these features provide a broad range of potential solutions to complex problems such as driver behaviour assessments and real-time object detection.
The Akida NSoC exemplifies ground-breaking Neural Processing computing tools for Edge AI systems and gadgets. Each Akida NSoC has 10 billion synapses and 1.2 million neurons, demonstrating orders of magnitude improved effectiveness than other neural processing devices available.
The unique combination of meagre power, high performance and on-chip learning enables for real-time processing at the sensor along with continuous learning. The objective is to facilitate personalisation of every driver’s understanding in real time & constant updates to the system with the change in the environmental conditions.
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