The brain is without doubt the world’s most powerful computer for solving problems in machine perception (vision, speech, language) and machine learning. Brains exist in 2+delta -dimensional physical space yet are capable of efficiently solving problems in higher dimensional spaces. We believe that the network structure of the brain architecture in 2+delta dimensional space contributes significantly to its effectiveness and energy efficiency in cognition. At all levels of the central nervous system, from retina to the cortex, the tissue is organized in a hierarchy of layers. In certain layers there is an abundance of axons, the physical structures in neurons responsible for communication” while others are densely packed with cell bodies and dendrites, or what one will consider as the computational” structures in the tissue. Furthermore, the layers are tightly coupled vertically through what is termed in biology a column”. Over the last half century computer scientists, architects and engineers have envisioned building computers that match the parallel processing capabilities of biological brains for perception and cognitive computing.

Three-dimensional integration through wafer stacking and 2.5D assembly is an alternative to technology scaling and monolithic integration that achieves increase in the number of transistors and short-range interconnect per unit area thus improving energy efficiency. To addres the challenges of rapid and flexible prototyping of large bioinspired systems for cognitive computing we abstract the 2+delta brain architectonics provide a guidance towards future development of silicon integrated systems for machine perception and learning that would be as effective and as efficient as biological brains. Neurochiplets SOC 2.5D architecture relies on this alternative approach to scaling, driven primarily by cost and flexible and rapid system level integration. 2.5D integration on a silicon interposer will interface the memory to neuromorphic chiplets and a commodity FPGA and processors (RISC-V) for operating system support and data I/O. In this talk I will discuss the design of three generations of bio-inspired 3D CMOS SOCs designed over a period of 15 years. I will present experimental data from the architectures and discuss successes and failures.

Biography:

Andreas G. Andreou is a professor of electrical and computer engineering, computer science and the Whitaker Biomedical Engineering Institute, at Johns Hopkins University. Andreou is the co-founder of the Johns Hopkins University Center for Language and Speech Processing. Research in the Andreou lab is aimed at brain inspired microsystems for sensory information and human language processing. Notable microsystems achievements over the last 25 years, include a contrast sensitive silicon retina, the first CMOS polarization sensitive imager, silicon rods in standard foundry CMOS for single photon detection, hybrid silicon/silicone chip-scale incubator, and a large-scale mixed analog/digital associative processor for character recognition. Significant algorithmic research contributions for speech recognition include the vocal tract normalization technique and heteroscedastic linear discriminant analysis, a derivation and generalization of Fisher discriminants in the maximum likelihood framework. In 1996 Andreou was elected as an IEEE Fellow, “for his contribution in energy efficient sensory Microsystems.”