Hardware Accelerated Similarity Search
Standard databases are composed of structured tables containing symbolic information.
The latest deep learning tools generate non-symbolic, high-dimensional feature vectors (x2vec) for images, video, text, music, speech, molecules, and many other objects. These representations are a more powerful method for achieving recognition accuracy, and more flexible than fixed symbolic representations.
However, searching for similar, non-symbolic, objects within billions of records – each with a high-dimensional representation – presents a computational challenge.
GSI technology has developed a breakthrough, patented technology: An in-memory associative processing chip based on in-place computing. It is capable of performing similarity search on billions of high-dimensional records, with very low latency (< 2 msec) and a high throughput of over 100,000 queries per second. The technology works with standard software frameworks such as TensorFlow.
The presentation will cover the technology, the product, and its performance.