The applications for artificial intelligence are wide and cover multiple domains including industry, health, home automation, consumer electronics, automotive, and smart cities. Application-specific integrated circuits performing tiny machine learning at ultra-low power and high accuracy are needed. The Von Neumann wall forces us to shift the processing elements closer to the memory to prevent data movement and therefore reduce energy consumption. Matrix-Vector Multiplication (MVM) can be achieved with many approaches that perform well with binary weights but not with multi-bit multiplications. This paper tries to highlight the advantages of using current sources to perform in-memory computing, improving further the energy consumption to perform multi-bit MVM.