When we think of the incredible capabilities of today’s artificial intelligence, we often imagine highly complex, indecipherable software code. However, the fundamental engine driving deep learning models—from image recognition to massive Large Language Models (LLMs)—is heavily reliant on the foundational laws of physics you likely learned in high school.

To sustain the exponential growth of AI and overcome the severe energy limitations of current hardware, the semiconductor industry is turning away from traditional digital logic and looking back to fundamental physics. The secret to the next generation of AI computing lies in Ohm’s Law.

The Matrix Math Bottleneck Beneath the surface, AI algorithms are fundamentally built on massive matrix operations, specifically multiply-accumulate (MAC) and vector-matrix multiplication (VMM) tasks. When an AI model processes data, it must adjust weights and biases through millions, or even billions, of simultaneous calculations. In massive models like GPT-3, vector-matrix multiplications account for an estimated 90% to 92% of the entire processing effort.

Traditional digital processors (CPUs and GPUs) execute these operations using complex logic gates and von Neumann architectures, which physically separate the computing unit from the memory. This means massive matrices of data must be continuously shuttled back and forth across a data bus just to perform basic math. This constant data commute—known as the von Neumann bottleneck—is incredibly inefficient, with data transfer consuming 10 to 100 times more energy than the actual computation.

Computing with Physics: The Analog Revolution To break this bottleneck, we must compute where the data lives. This is the core principle behind analog in-memory computing (IMC). By leveraging physical principles within a crossbar array architecture, IMC bypasses digital logic entirely and performs massively parallel matrix multiplications using physics.

Here is exactly how basic electrical laws replace complex digital logic:

• Ohm’s Law for Multiplication ($I = V \times G$): In an analog IMC crossbar array, the trained weights of an AI model are stored as a physical property—specifically, the electrical conductance (the inverse of resistance) of the memory cells. When input data is applied to the rows of the array as voltage pulses, it generates a current at each cross-point. According to Ohm’s Law, this resulting current is the natural mathematical multiplication of the input voltage and the stored conductance.
• Kirchhoff’s Current Law for Accumulation: The math does not stop at multiplication. As the newly generated currents flow down the columns of the crossbar array, they automatically sum together. Dictated by Kirchhoff’s Current Law, this physical addition perfectly executes the “accumulate” portion of the MAC operation in a single, instantaneous step.

Because these physical reactions occur simultaneously across the entire array, the vector-matrix multiplications are massively parallel, completely independent of the array’s size.

The Unprecedented Efficiency of Analog Systems This physical approach eliminates the need to constantly load and unload weights from off-chip memory, entirely bypassing the von Neumann bottleneck. While a traditional digital design requires hundreds of transistors just to perform basic multiplier and adder operations, this analog method achieves the exact same result utilizing highly efficient, simple memory cells.

By returning to these analog basics, brain-inspired IMC paradigms can achieve 100 to 1000 times higher energy efficiency than today’s traditional digital CPUs and GPUs.

Unlock True AI Efficiency with TetraMem Are you ready to break through the memory wall and stop wasting your power budget on digital data movement?

TetraMem is leading the analog computing revolution with its breakthrough multi-level RRAM (computing memristor) in-memory computing technology. Utilizing a proprietary One-Transistor-One-Resistor (1T1R) crossbar architecture, TetraMem computes directly where the data is stored, harnessing Ohm’s Law and Kirchhoff’s Current Law to execute massive AI matrix operations with unprecedented efficiency.

Backed by pioneering innovations recognized in Nature and Science, TetraMem’s highly scalable architecture is targeting over 300 TOPS/W for next-generation AI workloads. From ultra-low-power edge sensors to high-throughput data center accelerators, TetraMem provides the ultimate hardware paradigm shift.

Don’t let legacy digital bottlenecks constrain your AI’s potential. Discover how TetraMem’s analog in-memory computing solutions can transform your hardware infrastructure today.