Embedded - Microprocessors

Part Number	Description / PDF	Quantity
AM1806BZCE4 Texas Instruments	MICROPROCESSOR, 32 BIT, SITARA,	97
AM3715CUSA Texas Instruments	AM3715 SITARA PROCESSOR: ARM COR	2159
AM5708BCBDJEAR Texas Instruments	MICROCONTROLLER ARM CORTEX	0
OMAPL138EZWTD4E Texas Instruments	IC MPU OMAP-L1X 456MHZ 361NFBGA	499
TMS38010NL Texas Instruments	MICRO PERIPHERAL IC, MOS, PDIP48	4237
AM5K2E04XABD4 Texas Instruments	SOC MPU KEYSTONE II 1089FCBGA	0
OMAP3530ECBB Texas Instruments	IC MPU OMAP-35XX 600MHZ 515FCBGA	334
AM1705BPTPD4 Texas Instruments	32-BIT, 456MHZ, CMOS, PQFP176	276
OMAP3530ECUS Texas Instruments	IC MPU OMAP-35XX 600MHZ 423FCBGA	87
OMAPL138EZCED4E Texas Instruments	IC MPU OMAP-L1X 456MHZ 361NFBGA	0
OMAP3530DCBC Texas Instruments	32-BIT, 600MHZ, CMOS, PBGA515	5014
XAM3358AZCE Texas Instruments	IC MPU SITARA 298NFBGA	0
AM5729BABCXEA Texas Instruments	AM5729BABCXEA	233
AM5729BABCX Texas Instruments	VAYU PG 2.0	238
OMAP3525ECBCA Texas Instruments	IC MPU OMAP-35XX 600MHZ 515FCBGA	0
AM3715CUS Texas Instruments	AM3715 SITARA PROCESSOR: ARM COR	1197
SN74ACT8997NT Texas Instruments	MICROPROCESSOR CIRCUIT PDIP28	2484
AM1705DPTPA3 Texas Instruments	IC MPU SITARA 375MHZ 176HLQFP	59
AM1806BZCEA3 Texas Instruments	RISC MPU, 32-BIT, 375MHZ	160
AM5716AABCD Texas Instruments	SITARA PROCESSOR	0

Embedded - Microprocessors

1. Overview

Embedded microprocessors are specialized computing units designed for dedicated control, monitoring, or processing tasks within electronic systems. Unlike general-purpose CPUs, they integrate processing cores, memory interfaces, and peripheral controllers into a single chip (SoC) to optimize performance, power efficiency, and cost for specific applications. These devices form the backbone of modern IoT, automotive systems, industrial automation, and consumer electronics.

2. Main Types and Functional Classification

Type	Functional Characteristics	Application Examples
ARM Cortex-M Series	Low-power 32-bit cores with real-time capabilities, optional DSP extensions	Smart sensors, wearables, motor control
PowerPC	High reliability, floating-point units, automotive/Aerospace focused	Vehicle ECUs, avionics systems
MIPS	Efficient pipelining, 32/64-bit variants for multimedia processing	Networking equipment, set-top boxes
RISC-V	Open instruction set, modular architecture for customization	AI accelerators, edge computing devices
x86 (Embedded)	PC compatibility, high processing power with integrated GPUs	Industrial PCs, medical imaging equipment

3. Architecture and Components

A typical embedded microprocessor contains:

Processing Core(s): RISC/Complex Instruction Set architectures with 1-8 cores
Memory Subsystem: Integrated SRAM, ROM, and external DRAM controllers
Peripheral Interfaces: UART, SPI, I2C, USB, CAN, PCIe, Ethernet MAC
Real-Time Components: Watchdog timers, PWM generators, ADC/DAC modules
Power Management: Multiple sleep modes, DVFS (Dynamic Voltage/Frequency Scaling)

4. Key Technical Specifications

Parameter	Description	Importance
Clock Frequency	Operating speed (10MHz-6GHz)	Determines processing throughput
Instruction Set	RISC vs CISC architecture	Affects code density and power consumption
TDP (Thermal Design Power)	Power consumption under load (100mW-50W)	Dictates thermal management requirements
Process Node	Manufacturing technology (28nm-5nm)	Impacts performance and energy efficiency
Memory Bandwidth	Data transfer rate between core and memory	Limits performance in data-intensive tasks

5. Application Domains

Consumer Electronics: Smartphones (application processors), home appliances
Automotive: ADAS controllers, engine management systems
Industrial: PLCs (Programmable Logic Controllers), robotics
Healthcare: MRI scanners, portable diagnostic devices
Communications: 5G base stations, optical network transceivers

6. Leading Manufacturers and Products

Manufacturer	Representative Product	Key Features
ARM Holdings	Cortex-A78	128-bit NEON engine, 4nm process, 3.0GHz
Intel	Atom x6425E	Quad-core, 12W TDP, integrated Intel HD Graphics
NXP Semiconductors	i.MX 8M Plus	1.8GHz Cortex-A53, NPU for ML acceleration
Microchip	SAM9X60	32-bit ARM926EJ-S, 120MHz, ECC memory support
Qualcomm	QCS610	Hexagon DSP, Adreno GPU, AI Engine for computer vision

7. Selection Guidelines

Key considerations include:

Performance requirements vs power budget (e.g., Cortex-M55 for ultra-low-power AI)
Real-time constraints (deterministic latency for motor control applications)
Peripheral integration (CAN FD for automotive networks)
Software ecosystem (RTOS support, middleware availability)
Longevity and supply chain stability (automotive-grade qualification)

Case Study: A smart thermostat design selected NXP's MCIMX7U5 (Cortex-M4 + Cortex-A7) for its combination of real-time sensor processing and application-layer connectivity.

8. Industry Trends

Emerging directions include:

AI integration: On-chip neural processing units (NPUs) for edge ML inference
Heterogeneous computing: Multi-architecture SoCs (RISC-V + GPU + NPU)
Advanced packaging: 3D-stacked memory integration for bandwidth-intensive applications
Functional safety: ISO 26262 compliance for autonomous vehicle systems
Open ecosystems: Growth of RISC-V adoption in custom ASIC designs